# Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic
Segmentation

Cristiano Saltori1, Aljoša Ošep2, Elisa Ricci1,3, Laura Leal-Taixé4
1 University of Trento, 2 TU Munich, 3 Federazione Bruno Kessler, 4 NVIDIA
<EMAIL_ADDRESS>
###### Abstract

The ability to deploy robots that can operate safely in diverse environments
is crucial for developing embodied intelligent agents. As a community, we have
made tremendous progress in within-domain LiDAR semantic segmentation.
However, do these methods generalize across domains? To answer this question,
we design the first experimental setup for studying domain generalization (DG)
for LiDAR semantic segmentation (DG-LSS). Our results confirm a significant
gap between methods, evaluated in a cross-domain setting: for example, a model
trained on the source dataset (SemanticKITTI) obtains $26.53$ mIoU on the
target data, compared to $48.49$ mIoU obtained by the model trained on the
target domain (nuScenes). To tackle this gap, we propose the first method
specifically designed for DG-LSS, which obtains $34.88$ mIoU on the target
domain, outperforming all baselines. Our method augments a sparse-
convolutional encoder-decoder 3D segmentation network with an additional,
dense 2D convolutional decoder that learns to classify a birds-eye view of the
point cloud. This simple auxiliary task encourages the 3D network to learn
features that are robust to sensor placement shifts and resolution, and are
transferable across domains. With this work, we aim to inspire the community
to develop and evaluate future models in such cross-domain conditions.

###### Abstract

We provide supplementary material in support of the main paper. We organize
the content as follows:

  * •

In Sec. A, we report the implementation details of our experiments, the
additional experimental evaluations on Synth4D-nuScenes$\to$Real, and an
additional ablation of LiDOG.

  * •

In Sec. B, we report and discuss qualitative results comparing the performance
of LiDOG with all the compared baselines and ground truth annotations.

Figure 1: Domain Generalization for LiDAR Semantic Segmentation (DG-LSS).
Left: Existing LSS methods are trained and evaluated on point clouds drawn
from the same domain. Right: We focus on studying LSS under domain shifts,
where the test samples are drawn from a different data distribution. Our paper
aims to address the generalization aspect of this task.

##  1 Introduction

We address the challenge of achieving accurate and robust semantic
segmentation of LiDAR point clouds. LiDAR semantic segmentation (LSS) is one
of the most fundamental perception problems in mobile robot navigation, with
applications ranging from mapping [29], localization [33], and online dynamic
situational awareness [46].111Semantic point classifier was part of the
perception stack in early autonomous vehicles, such as Stanley, that won the
DARPA challenge in 2005.

Status Quo. State-of-the-art LSS methods [8, 16, 45, 69] perform well when
trained and evaluated using the same sensory setup and environment (_i.e_.,
source domain, Fig. 1, left). However, their performance degrades
significantly in the presence of domain shifts (Fig. 1, right), commonly
caused by differences in sensory settings, _e.g_., a new type of sensor, or
recording environments, _e.g_., geographic regions with different road layouts
or types of vehicles. One way to mitigate this is to collect multi-domain
datasets for pre-training, similar to what has been done in the image domain
using inexpensive cameras that are widely available [9, 31]. However, building
a crowd-sourced collection of multi-domain LiDAR datasets is currently not
feasible.

Stirring the pot. As a first step towards LiDAR segmentation models that are
robust to domain shifts, we present the first-of-its-kind experimental test-
bed for studying Domain Generalization (DG) in the context of LiDAR Semantic
Segmentation (LSS). In our DG-LSS setup we train and evaluate models on
different domains, including two synthetic [40] and two real-world densely
labeled datasets [3, 13], recorded in different geographic regions with
different sensors. This evaluation setup reveals a significant gap in terms of
mean intersection-over-union between models trained on the source and target
domains: for example, a model transferred from SemanticKITTI to nuScenes
dataset obtains $26.53$ mIoU compared $48.49$ mIoU obtained by the model fully
trained on the target domain. Could this gap be alleviated with DG techniques?

Insights. To address this challenge, we propose LiDOG (LiDAR DOmain
Generalization) as a simple yet effective method specifically designed for DG-
LSS. In addition to reasoning about the scene semantics in 3D space, LiDOG
projects features from the sparse 3D decoder onto the 2D bird’s-eye-view (BEV)
plane along the vertical axis and learns to estimate a dense 2D semantic
layout of the scene. In this way, LiDOG encourages the 3D network to learn
features that are robust to variations in, _e.g_., type of sensor or geo
locations, and thereby can be transferred across different domains. This
directly leads to increased robustness toward domain shifts and yields $+8.35$
mIoU improvement on the target domain, confirming the efficacy of our
approach. Our experimental evaluation confirms this approach is consistently
more effective compared to prior efforts in data augmentations [62, 35, 39],
domain adaptation techniques [50, 23], and image-based DG techniques [7, 36],
applied to the LiDAR semantic segmentation.

Contributions. We make the following key contributions. We (i) present the
first study on domain generalization in the context of LiDAR semantic
segmentation. To this end, we (ii) carefully construct a test-bed for studying
DG-LSS using two synthetic and two densely-labeled real-world datasets
recorded in different cities with different sensors. This allows us to (iii)
rigorously study how prior efforts proposed in related domains can be used to
tackle domain shift and (iv) propose LiDOG, a simple yet strong baseline that
learns robust, generalizable, and domain-invariant features by learning
semantic priors in the 2D birds-eye view. Despite its simplicity, we achieve
state-of-the art performance in all the generalization directions. 222Our code
is available at https://saltoricristiano.github.io/lidog/.

##  2 Related Work

Figure 2: LiDOG overview. We encode our input LiDAR scan $P_{j}$ using the 3D
backbone $g^{3D}$ to learn the occupied voxels’ feature representations
$F^{3D}$. (Upper branch - main task) We apply a sparse segmentation head on
$F^{3D}$ and supervise with 3D semantic labels, $\mathcal{Y}_{j}^{3D}$. (Lower
branch - auxiliary task) We project those features along the height-axis to
obtain a dense 2D bird’s-eye (BEV) view features $F^{BEV}$, and apply several
2D convolutional layers to learn the 2D BEV representation. We supervise the
BEV auxiliary task by using BEV-view of semantic labels,
$\mathcal{Y}_{j}^{BEV}$. We train jointly on both $L^{3D}$ and $L^{BEV}$.

LiDAR Semantic Segmentation (LSS).  LiDAR-based semantic scene understanding
has been used in autonomous vehicle perception since the dawn of autonomous
driving [46]. State-of-the-art methods are data-driven and rely on different
LiDAR data representations and network architectures. Point-based methods [37,
16, 45, 64, 53] learn representations directly from unordered point sets,
whereas range-view-based methods [34, 51, 52, 63, 1] learn a representation
from a range pseudo-image that represents the LiDAR point cloud. State-of-the-
art methods [8, 15, 69, 28, 44] map 3D points into a 3D quantized grid and
learn representations using sparse 3D convolutions [15, 8]. The works
mentioned above focus on within-domain performance, _i.e_., they train and
test on data from the same dataset and do not evaluate robustness to domain
shifts. By contrast, we focus on cross-domain robustness, _i.e_., we train on
a source domain and test on a target domain.

Domain Adaptation for LSS.  Domain shift in LiDAR perception [50, 41, 24, 38]
is a well-documented issue as sensors are expensive, and crowd-sourcing data
collection is difficult. Unsupervised domain adaptation (UDA) techniques allow
us to reduce the domain shift by looking at data from the target domain, but
without needing any labels in that domain. Several methods reduce domain shift
by mimicking sensory characteristics of the target domain data, often in a
data-driven manner [23, 65, 55, 56]. Alternatively, domain shift can be
reduced by feature alignment or prediction consistency [52, 20, 19], or by
training a point cloud completion network to map domains into a canonical
domain [59]. More recently, mixup strategies [61, 60] have been introduced in
the context of UDA for LSS [39, 54]. In LiDAR-based 3D object detection,
scaling [50] and self-training [41, 58] are often used strategies. All
aforementioned methods must be exposed to (unlabeled) data from the target
domain to adapt the learned representation. In this work, we study DG-LSS with
the goal of learning as-general-and-robust-as-possible representations in the
first place, before exposure to any data from a particular target domain
during training.

Domain Generalization.  Alternatively, robustness to domain shifts can be
tackled via Domain Generalization (DG). In DG, we aim to learn domain-
invariant features by training solely on the source domain data [67]. Due to
the importance of learning robust representations, DG has been widely studied
in the image domain [7, 67, 66, 36] in both, multi- [14, 27, 68] and single-
source [5, 4, 66, 49, 25] settings. Image-based DG approaches reduce domain
shift via domain alignment [30, 14], meta-learning [27, 2], data and style
augmentations [43, 68, 66], ensembling techniques [11, 32], disentangled
learning [21, 48], self-supervised learning [5, 4], regularization strategies
[49, 17] and, reinforcement learning [17, 25]. Data augmentations have also
been employed in the context of DG in LiDAR-based 3D object detection in [26]
to learn to detect vehicles that have been damaged in car accidents. In our
work, we propose the first method specifically designed for DG-LSS and
introduce a dense 2D convolutional decoder that learns features robust to
domain shift. In our experimental evaluation (Sec. 5), we adapt and compare to
several state-of-the-art data augmentation techniques, such as Mix3D [35],
PointCutMix [62], and PolarMix [54] that apply mixup strategies [61, 60] to 3D
point clouds. Moreover, we adapt state-of-the-art methods from the image
domain RobustNet [7] and IBN [36]. We show that our method has substantially
better generalization across all experiments. Finally, parallel work by [42]
accumulates consecutive point clouds over time to introduce robustness towards
point clouds of different domains and, consequentially, improve generalization
capabilities.

##  3 A LiDAR Journey Across Domains

In this section, we first recap the standard LiDAR Semantic Segmentation (LSS)
setting for completeness. Next, we formally introduce the problem of Domain
Generalization for LiDAR Semantic Segmentation (DG-LSS), which we study in
this paper.

LiDAR Semantic Segmentation.  In LSS, we are given a set of labeled training
instances in the form of point clouds
$\mathcal{T}_{train}=\\{P_{j}=\\{\mathbf{p}_{i}\in\mathbb{R}^{3}\\}_{i=1}^{N_{j}}\\}$.
Each point cloud $P_{j}\sim\mathcal{D}$ consists of $N_{j}$ points, and is
drawn from a certain point cloud distribution $\mathcal{D}$. Moreover, point
clouds in the training set are labeled according to a predefined semantic
class vocabulary $\mathcal{K}=\\{1,\ldots,K\\}$ of $K$ categorical labels.
Given such a training set $\mathcal{T}_{train}$ along with class vocabulary
$\mathcal{K}$, the goal of LSS is to learn a function
$f(\mathbf{p}_{i})\to\mathcal{K}$, that maps each point $\mathbf{p}_{i}\in
P_{j},\forall j,$ to one of $K$ semantic classes, such that the prediction
error on the same domain $\mathcal{D}$ is minimized. In other words, LSS
models are trained and evaluated on (disjunct) sets of point clouds that are
drawn from the same data distribution $\mathcal{D}$. We note that it is
standard practice for LSS methods to be evaluated on multiple datasets333At
the moment, SemanticKITTI and nuScenes-lidarseg are commonly used for the
evaluation.. However, standard multi-dataset evaluation follows the setting
described above: models are trained and evaluated for each domain separately.

Domain Generalization for LiDAR Semantic Segmentation.  In this work, we
release the assumption that train and test splits were sampled from the same
domain and explicitly study cross-domain generalization in LSS. We are given
$M$ labeled datasets $\mathcal{T}_{m}\sim\mathcal{D}_{m}$,
$m\in\\{1,\ldots,M\\}$ sampled from different data distributions
$\mathcal{D}_{m}$. Importantly, all datasets must be labeled according to a
common semantic vocabulary $\mathcal{K}$, _i.e_., we are studying DG in
closed-set conditions [67]. Then, the task can be defined as follows: we use
$\mathcal{T}_{\\{m\\}}$ datasets for training, and the held-out datasets
$\mathcal{T}_{\\{m^{\prime}\\}},\\{m^{\prime}\\}\cap\\{m\\}=\emptyset$, solely
for testing.

The goal of DG-LSS is, therefore, to train a mapping
$f(\mathbf{p}_{i})\to\mathcal{K}$, such that the prediction performance on the
target datasets
$\mathcal{T}_{\\{m^{\prime}\\}}\sim\mathcal{D}_{\\{m^{\prime}\\}}$ is
maximized. We only have access to data and labels from the target datasets
during evaluation. The model has never seen any target data before evaluation,
neither labeled nor unlabeled.

##  4 Walking Your LiDOG

Overview.  We provide a schematic overview of LiDOG: Lidar DOmain
Generalization network in Fig. 2. LiDOG leverages a 3D sparse-convolutional
encoder-decoder neural network (Fig. 2, $g^{3D}$) that encodes the input point
cloud $P_{j}$ into sparse 3D features $F^{3D}$ (Fig. 2). In order to tackle
the domain shift problem, we propose to augment this network with a dense top-
down prediction auxiliary task during model training. In particular, we
project sparse 3D features $F^{3D}$ from the decoder network along the height
axis to obtain a 2D bird’s-eye view (BEV) representation $F^{BEV}$ of the
features learned by the 3D network. Then, we jointly optimize the losses for
the 3D and BEV network heads. Importantly, the auxiliary BEV head is not
required during model inference. Its sole purpose is to effectively learn
domain-agnostic features during training.

###  4.1 3D Segmentation Branch

The upper branch of LiDOG (Fig. 2) aims to learn the main segmentation task,
_i.e_., it learns the mapping function $f(\mathbf{p}_{i})\to\mathcal{K}$. We
first process the input LiDAR point cloud $P_{j}$ into a 3D occupancy (voxel)
grid $V_{j}$. During this phase, the continuous 3D input space is evenly
sampled into discrete 3D cells. Points falling into the same cell are merged.
We then learn a feature representation of the occupancy grid $V_{j}$ via a 3D
encoder-decoder network $g^{3D}$ based on the well-established sparse-
convolutional backbone [8]. The encoder consists of a series of sparse 3D
convolutional downsampling layers that learn a condensed 3D representation,
while the decoder upsamples the features back to the original resolution with
sparse convolutional upsampling layers. We use sparse batch-normalization
layers. The output of $g^{3D}$ is a set of voxel-wise features
$F^{3D}_{j}=g^{3D}(V_{j})$, where each feature vector is associated with a
voxel cell in $V_{j}$. We employ a sparse segmentation head to obtain semantic
posteriors that predict the voxel-wise categorical distribution from the voxel
features $F^{3D}_{j}$:
$\tilde{\mathcal{Y}^{3D}_{j}}=p(\mathcal{K}|V_{j})=\sigma(h^{3D}(F^{3D}_{j}))$,
where $\sigma$ denotes the softmax activation function.

###  4.2 Dense Auxiliary Task

In the lower branch of LiDOG, we add the auxiliary task that encourages the
network to learn a representation that is robust to domain shifts. We
transform 3D features to bird’s-eye view (BEV) space, and train a network to
estimate BEV semantic scene layout.

Sparse-to-dense feature projection.  We start our dense-to-sparse segmentation
task by projecting voxel-wise features $F_{j}^{3D}$ into dense BEV-features
feature space $F_{j}^{BEV}$. Given a voxel $v_{i}\in V_{j}$ center coordinates
$(x_{i},y_{i},z_{i})$, we compute its corresponding BEV coordinates
$(x^{BEV}_{i},z^{BEV}_{i})$ as:

$q(x_{i},z_{i})=(x^{BEV}_{i},z^{BEV}_{i})=\Bigl{(}\Bigl{\lfloor}\frac{x_{i}}{x_{q}}\Bigl{\rfloor},\Bigl{\lfloor}\frac{z_{i}}{z_{q}}\Bigl{\rfloor}\Bigr{)},$
(1)

where $q$ is the projection function, and $x_{q}$ and $z_{q}$ are the
quantization parameters. We project only voxels within projection bounds
$B^{3D}=[(-b_{x},b_{x}),(-b_{z},b_{z})]$, where $b_{x}$ and $b_{z}$ are the
bound values along the $x$ and $z$ axis, and shift projected coordinates to
fit in the BEV-features height and width. Consequently, we define BEV features
$F_{j}^{BEV}$ by initializing them to zero and by filling non-empty cells as
$F_{j}^{BEV}(x^{BEV}_{i},z^{BEV}_{i})=F^{3D}_{i}$. When multiple voxels
project to the same cell, we randomly keep one.

Sparse-to-dense label projection.  We obtain source labels by following the
same procedure used for $F_{j}^{BEV}$. After voxelization, each voxel $v_{i}$
is associated to a label category $y^{3D}_{i}\in\mathcal{Y}^{3D}_{j}$. We use
Eq. 1 and the same bounds $B^{3D}$ to compute the label coordinates
$(x^{BEV}_{i},z^{BEV}_{i})$ of BEV image pixels. Then, we define BEV labels
as: $\mathcal{Y}_{j}^{BEV}(x^{BEV}_{i},z^{BEV}_{i})=y^{3D}_{i}$.

BEV predictions.  We predict BEV semantic posteriors by employing a standard
2D segmentation head $h^{BEV}$ which predicts semantic classes in the dense 2D
BEV plane and takes as input BEV features $F_{j}^{BEV}$. First, we reduce the
dimensionality of $F_{j}^{BEV}$ through a pooling operation. Then, we feed
$h^{BEV}$ with $F_{j}^{BEV}$ and obtain BEV semantic predictions:
$\tilde{\mathcal{Y}^{BEV}_{j}}=\sigma(h^{BEV}(F_{j}^{BEV}))$, where $\sigma$
denotes the softmax activation function.

###  4.3 Losses

Given 3D semantic predictions $\tilde{\mathcal{Y}_{j}^{3D}}$ and labels
$\mathcal{Y}_{j}^{3D}$, as well as BEV semantic predictions
$\tilde{\mathcal{Y}_{j}^{BEV}}$ and labels $\mathcal{Y}_{j}^{BEV}$, we train
both LiDOG network heads jointly using soft DICE loss [18], _i.e_.,
$L^{3D}=dice(\tilde{\mathcal{Y}_{j}^{3D}},\mathcal{Y}_{j}^{3D})$, and
$L^{BEV}=dice(\tilde{\mathcal{Y}_{j}^{BEV}},\mathcal{Y}_{j}^{BEV})$. Finally,
we average contributions of both losses:
$L_{tot}=\frac{1}{2}(L^{BEV}+L^{3D})$. DICE loss has been shown in literature
[18, 39] to perform favorably for rarer classes compared to the cross-entropy
loss.

\begin{overpic}[width=60.70653pt]{fig/differences/kitti_3d.png} \end{overpic} |  \begin{overpic}[width=36.8573pt]{fig/differences/kitti_bev.png} \end{overpic} |  \begin{overpic}[width=60.70653pt]{fig/differences/nusc_3d.png} \end{overpic} |  \begin{overpic}[width=34.69038pt]{fig/differences/nusc_bev.png} \end{overpic}
---|---|---|---
Figure 3: LiDAR point clouds and their corresponding BEV views: SemanticKITTI
(left) and nuScenes (right). After projection, BEV images are geometrically
more similar.

###  4.4 Why Does This Work?

We demonstrate empirically in Sec. 5 that the dense BEV prediction proxy task
significantly improves model performance in terms of cross-domain
generalization. Why does this simple objective yield, in practice, more robust
feature representations? There are several possible sources of domain shift,
_e.g_., different sensors, different environments, and scans are visibly
different (Fig. 3). After the projection, we obtain a denser label space that
is less sensitive to individual sensor characteristics: in the BEV space scans
from different domains look more alike, Fig. 3. We effectively regularize the
model training by promoting the network robustness to changes in the sensor
acquisition patterns. We validate this intuition in Fig. 4 by visualizing
learned point embeddings [47] for the road class with and without BEV
auxiliary task, across multiple domains. As can be seen, projected embeddings
are consistently closer when employing BEV auxiliary task.

\begin{overpic}[width=99.73404pt]{fig/rebuttal/synth2kitti/source_tsne.png} \put(2.0,65.0){\color[rgb]{0,0,0}\footnotesize w/o BEV} \put(-7.0,2.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize Synth4D-k.$\to$S.KITTI}} \end{overpic} | \begin{overpic}[width=99.73404pt]{fig/rebuttal/synth2kitti/lidog_tsne.png} \put(2.0,65.0){\color[rgb]{0,0,0}\footnotesize BEV} \end{overpic}
---|---
\begin{overpic}[width=99.73404pt]{fig/rebuttal/synth2nusc/source_tsne.png} \put(2.0,65.0){\color[rgb]{0,0,0}\footnotesize w/o BEV} \put(-7.0,5.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize Synth4D-k.$\to$nuSc.}} \end{overpic} | \begin{overpic}[width=99.73404pt]{fig/rebuttal/synth2nusc/lidog_tsne.png} \put(2.0,65.0){\color[rgb]{0,0,0}\footnotesize BEV} \end{overpic}
Figure 4: Feature visualization: t-SNE visualization [47] of the point
embeddings for the road class, obtained by training our network without (w/o
BEV, left) and with BEV task (BEV, right). Top:
Synth4D-KITTI$\to$SemanticKITTI. Bottom: Synth4D-KITTI$\to$nuScenes. As shown,
projected features are well-aligned when training the network with the
auxiliary BEV task.

##  5 Experimental Evaluation

In this section, we report our experimental evaluation. In Sec. 5.1, we
describe our evaluation protocol, datasets and baselines. In Sec. 5.2-5.3, we
report our synth-to-real and real-to-real results, and discuss qualitative
results. In Sec. 5.4, we ablate our proposed approach. We provide
implementation details in the appendix.

###  5.1 Experimental Protocol

We base our evaluation protocol on two synthetic (Synth4D [40]) and two real-
world datasets (SemanticKITTI [3] and nuScenes [13]). We study (i)
generalization from synthetic$\rightarrow$real data in single- and multi-
source settings, _i.e_., trained on one or multiple source datasets, as well
as (ii) real$\rightarrow$real generalization.

Synthetic datasets.  Synth4D [40] is a synthetic dataset generated using CARLA
[10] simulator. It provides two synthetic source domains, each one with $20k$
labeled scans, emulating Velodyne64HDLE and Velodyne32HDLE sensors, similar to
those used to record SemanticKITTI (Synth4D-KITTI) and nuScenes-lidarseg
datasets (Synth4D-nuScenes). These datasets were generated in the same
synthetic environment, mimicking different sensor types. We use the official
training splits in all experiments.

Real datasets.  SemanticKITTI [3] was recorded in various regions of
Karlsruhe, Germany, with Velodyne64HDLE sensor, and provides $20k$ labeled
scans. nuScenes [13] was recorded in Boston, USA, and Singapore, with a
sparser Velodyne32HDLE sensor and provides $40k$ labeled LiDAR scans. We use
the official training and validation splits for both datasets in all
experiments.

Class vocabulary.  To ensure this task is well-defined, we formalize cross-
dataset consistent and compatible semantic class vocabulary, which ensures
there is a one-to-one mapping between all semantic classes. We follow [40]
which provides a common label space among Synth4D, SemanticKITTI and nuScenes-
lidarseg consisting of seven semantic classes: vehicle, person, road,
sidewalk, terrain, manmade and vegetation. We perform all evaluation with
respect to these classes.

Baselines.  We are the first in studying DG-LSS, therefore, we construct our
set of baselines by considering the previous efforts in bridging the domain
shift in other LiDAR tasks as well DG methods for images.

Data augmentation baselines (Aug).  Data augmentations are used in supervised
learning to improve model generalization and, in DA, to reduce domain shift.
We re-implement and evaluate in the DG context three data augmentation
approaches: Mix3D [35], PointCutMix [62] and CoSMix [39]. Mix3D concatenates
both points and labels of different scenes, obtaining a single mixed-up scene.
PointCutMix and CoSMix follow the same strategy but mix either patches or
semantic regions, respectively. We follow the official implementations and
apply these data augmentation strategies during source training. For single-
source training, we mix between samples of the same domain, while for multi-
source training, we mix between samples of the two source domains. Image-based
baselines (2D DG).  Image-based DG is a widely explored field. However, not
all the methods can be extended to DG-LSS due to their image-related
assumptions, _e.g_., style consistency. We identify and re-implement two
image-based approaches that can be extended to DG-LSS: IBN [36] and RobustNet
[7]. IBN makes use of both batch and instance normalization layers in the
network blocks to boost generalization performance. We follow the IBN-C block
scheme and use it to build the sparse model. RobustNet [7] makes use of IBN
blocks and introduces an instance whitening loss. Similarly, we start from our
IBN implementation and use their instance-relaxed whitening loss [7] during
training.

UDA for LiDAR baselines (3D UDA).  Most of the UDA for LSS approaches assume
(unlabeled) target data available for training/adaptation [58], hence, we
cannot adapt and evaluate them in the DG setting fairly. We identify two UDA
baselines that assume only weak supervision from the target domain, _e.g_.,
vehicle dimensions or sensor specs. We re-implement and extend SN [50] and
RayCast [24]. SN [50] uses the knowledge of average vehicle dimensions from
source and target domains to re-scale source instances. We employ DBSCAN to
isolate vehicle instances in both source and target domains and estimate the
ratio between their dimensions. At training time, we follow their pipeline and
train our segmentation model on the re-scaled point clouds. RayCast [23]
employs ray casting to re-sample source data by mimicking target sensor
sampling. We use the official code to re-sample source data and use mapped
data during training. Notice that both these baselines use a-priori knowledge
from the target domain, _i.e_., vehicle dimensions or target sensor
specifications, and can thus be considered as weakly-supervised baselines.

###  5.2 Synth $\to$ Real Evaluation

Table 1: Synth4D-KITTI$\rightarrow$Real, single-source. Our approach (LiDOG)
improves upon Source model on both real datasets: $+19.49$ mIoU for
SemanticKITTI and $+16.52$ mIoU for nuScenes, outperforming all baselines.
Lower bound (red): a model trained on the source domain without the help of DG
techniques. Upper bound (blue): model directly trained on the target data.

S | T | Info | Method | Vehicle | Person | Road | Sidewalk | Terrain | Manmade | Vegetation | mIoU
---|---|---|---|---|---|---|---|---|---|---|---
|  | Lower bound | Source | 34.33 | 4.47 | 36.38 | 13.27 | 19.48 | 23.1 | 41.81 | 24.69
Synth4D-KITTI |  SemanticKITTI | Aug |  Mix3D [35] | 59.99 | 14.02 | 55.75 | 17.71 | 25.67 | 39.26 | 53.00 | 37.92
PointCutMix [62] | 58.26 | 14.35 | 67.74 | 13.91 | 27.26 | 49.21 | 64.22 | 42.14
CoSMix [39] | 43,01 | 14.16 | 61.84 | 12.38 | 18.84 | 18.00 | 57.56 | 32.26
2D DG |  IBN [36] | 24.95 | 9.18 | 56.82 | 17.85 | 7.21 | 21.59 | 44.65 | 26.04
RobustNet [7] | 50.85 | 14.97 | 58.71 | 7.83 | 19.96 | 42.58 | 44.52 | 34.20
3D UDA |  SN [50] | 49,38 | 14.83 | 68.53 | 18.45 | 25.62 | 37.49 | 59.48 | 39.11
RayCast [24] | 51.73 | 4.33 | 56.43 | 18.07 | 23.91 | 36.23 | 40.52 | 33.03
3D DG | Ours (LiDOG) | 72.86 | 17.1 | 71.85 | 28.48 | 11.53 | 46.02 | 61.42 | 44.18
Upper bound | Target | 81.57 | 23.23 | 82.09 | 57.64 | 46.84 | 64.14 | 75.21 | 61.53
|  | Lower bound | Source | 19.99 | 6.80 | 32.85 | 6.81 | 11.95 | 45.07 | 20.86 | 20.62
Synth4D-KITTI |  nuScenes | Aug |  Mix3D [35] | 26,8 | 22.68 | 41.90 | 7.31 | 13.54 | 48.17 | 52.09 | 30.36
PointCutMix [62] | 23,97 | 19.49 | 44.27 | 6.29 | 12.14 | 48.85 | 55.44 | 30.06
CoSMix [39] | 16.94 | 15.78 | 52.97 | 2.09 | 5.55 | 15.6 | 43.85 | 21.83
2D DG |  IBN [36] | 21,32 | 11.96 | 36.83 | 5.39 | 11.25 | 35.91 | 37.46 | 22.87
RobustNet [7] | 26.19 | 7.47 | 43.85 | 2.29 | 13.93 | 43.63 | 46.32 | 26.24
3D UDA |  SN [50] | 23.14 | 14.08 | 51.60 | 11.38 | 14.02 | 46.91 | 50.83 | 30.28
RayCast [24] | 19.54 | 11.78 | 56.53 | 6.66 | 8.45 | 45.66 | 36.99 | 26.52
3D DG | Ours (LiDOG) | 31.29 | 19.62 | 64.66 | 14.21 | 15.63 | 57.3 | 57.27 | 37.14
Upper bound | Target | 47.42 | 20.18 | 80.89 | 37.02 | 34.99 | 64.27 | 54.67 | 48.49

\begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2kitti/source.jpg} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2kitti/mix3d.jpg} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2kitti/our.jpg} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2kitti/gt.jpg} \end{overpic}
---|---|---|---
\begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2nusc/source.jpg} \put(40.0,-5.0){\color[rgb]{0,0,0}\footnotesize{source}} \put(145.0,-5.0){\color[rgb]{0,0,0}\footnotesize{mix3D}} \put(250.0,-5.0){\color[rgb]{0,0,0}\footnotesize{ours}} \put(350.0,-5.0){\color[rgb]{0,0,0}\footnotesize{gt}} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2nusc/mix3d.jpg} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2nusc/our.jpg} \end{overpic} | \begin{overpic}[width=119.24821pt]{fig/qualitative/main/synth4dkitti2nusc/gt.jpg} \end{overpic}
Figure 5: Qualitative results. Top: Synth4D-KITTI$\rightarrow$SemanticKITTI,
bottom: Synth4D-KITTI$\rightarrow$nuScenes. LiDOG improves consistently
improves results over the source model and outperforms Mix3D [35] with more
homogeneous predictions.

In this section, we train our model on one or more synthetic source domains
and evaluate on both real target domains, _i.e_., SemanticKITTI and nuScenes-
lidarseg. We report two models as lower and upper bounds in all the tables for
reference: source (_i.e_., a model trained on the source domain without the
help of DG techniques) and target (_i.e_., model directly trained on the
target data).

Table 2: (Synth4D-nuScenes $+$ Synth4D-KITTI)$\rightarrow$Real, multi-source.
Baselines significantly improve performance relative to the source model.
Specifically, with LiDOG we observe $+10.62$ mIoU improvement on SemanticKITTI
and $+14.63$ mIoU on nuScenes. Our approach (LiDOG) outperforms all the
compared approaches. Lower bound (red): a model trained on the source domain
without the help of DG techniques. Upper bound (blue): model directly trained
on the target data.

S | T | Info | Method | Vehicle | Person | Road | Sidewalk | Terrain | Manmade | Vegetation | mIoU
---|---|---|---|---|---|---|---|---|---|---|---
Synth4D-nuScenes+Synth4D-KITTI |  | Lower bound | Source | 45.14 | 8.39 | 41.74 | 17.28 | 18.44 | 33.81 | 57.93 | 31.82
SemanticKITTI | Aug |  Mix3D [35] | 45.64 | 7.72 | 59.11 | 22.12 | 16.86 | 50.03 | 51.59 | 36.15
PointCutMix [62] | 48.38 | 6.28 | 60.8 | 20.53 | 7.56 | 33.62 | 54.32 | 33.07
CoSMix [39] | 45.81 | 0.93 | 59.28 | 22.81 | 18.13 | 43.14 | 40.91 | 33.00
2D DG |  IBN [36] | 58.9 | 12.02 | 67.09 | 27.89 | 7.53 | 33.91 | 57.62 | 37.85
RobustNet [7] | 59.28 | 13.11 | 66.55 | 7.75 | 30.87 | 35.46 | 62.75 | 39.40
3D UDA |  SN [50] | 35.07 | 5.95 | 60.36 | 27.96 | 13.3 | 26.88 | 54.88 | 32.06
RayCast [24] | 58.06 | 3.3 | 63.41 | 25.33 | 22.03 | 35.39 | 41.64 | 35.59
3D DG | Ours (LiDOG) | 66.23 | 18.87 | 67.39 | 24.13 | 15.22 | 46.21 | 59.03 | 42.44
Upper bound | Target | 81.57 | 23.23 | 82.09 | 57.64 | 46.84 | 64.14 | 75.21 | 61.53
Synth4D-nuScenes+Synth4D-KITTI |  | Lower bound | Source | 20.64 | 13.4 | 28.44 | 7.61 | 10.23 | 52.88 | 46.01 | 25.60
nuScenes | Aug |  Mix3D [35] | 28.35 | 24.6 | 57.16 | 13.96 | 9.48 | 60.19 | 55.98 | 35.67
PointCutMix [62] | 25.6 | 14.8 | 54.29 | 11.58 | 6.66 | 58.09 | 51.53 | 31.79
CoSMix [39] | 23.13 | 8.26 | 52.24 | 11.04 | 10.88 | 58.29 | 53.43 | 31.04
2D DG |  IBN [36] | 26.11 | 16.95 | 55.16 | 13.59 | 12.67 | 56.64 | 52.92 | 33.43
RobustNet [7] | 27.03 | 16.48 | 50.61 | 6.66 | 15.73 | 57.68 | 56.47 | 32.95
3D UDA |  SN [50] | 19.08 | 13.89 | 53.18 | 14.73 | 9.69 | 53.54 | 45.39 | 29.93
RayCast [24] | 25.37 | 9.03 | 60.27 | 10.03 | 11.15 | 54.72 | 45.56 | 30.87
3D DG | Ours (LiDOG) | 30.78 | 25.3 | 73.2 | 19.83 | 16.03 | 62.65 | 53.80 | 40.23
Upper bound | Target | 47.42 | 20.18 | 80.89 | 37.02 | 34.99 | 64.27 | 54.67 | 48.49

Table 3: SemanticKITTI$\rightarrow$nuScenes, single-source. We train our model
on SemanticKITTI and evaluate it on the nuScenes dataset. LiDOG improves over
the source model by $+8.35$ mIoU. Lower bound (red): a model trained on the
source domain with-out the help of DG techniques. Upper bound (blue): model
directly trained on the target data.

S | T | Info | Method | Vehicle | Person | Road | Sidewalk | Terrain | Manmade | Vegetation | mIoU
---|---|---|---|---|---|---|---|---|---|---|---
|  | Lower bound | Source | 22.92 | 0.03 | 63.33 | 16.09 | 7.42 | 35.40 | 40.53 | 26.53
SemanticKITTI |  nuScenes | Aug |  Mix3D [35] | 33.74 | 11.22 | 58.54 | 12.91 | 5.28 | 50.36 | 48.59 | 31.52
PointCutMix [62] | 22.75 | 2.68 | 59.37 | 10.47 | 7.04 | 27.9 | 42.74 | 24.71
CoSMix [39] | 35.91 | 0.00 | 58.13 | 11.57 | 8.95 | 45.17 | 49.11 | 29.83
2D DG |  IBN [36] | 29.93 | 0.01 | 56.77 | 18.70 | 12.09 | 37.67 | 33.83 | 27.00
RobustNet [7] | 25.50 | 0.03 | 62.40 | 15.12 | 9.40 | 29.58 | 43.62 | 26.52
3D UDA |  SN [50] | 21.35 | 0.01 | 60.48 | 15.06 | 6.16 | 31.85 | 45.69 | 25.80
RayCast [24] | 28.82 | 0.00 | 59.25 | 16.08 | 12.51 | 49.72 | 49.82 | 30.89
3D DG | Ours (LiDOG) | 23.97 | 14.86 | 70.63 | 24.59 | 13.97 | 45.27 | 50.85 | 34.88
Upper bound | Target | 47.42 | 20.18 | 80.89 | 37.02 | 34.99 | 64.27 | 54.67 | 48.49

Table 4: nuScenes$\rightarrow$SemanticKITTI, single-source. We train our model
on nuScenes and evaluate it on the SemanticKITTI dataset. LiDOG improves over
the source model by $+11.67$ mIoU. Lower bound (red): a model trained on the
source domain with-out the help of DG techniques. Upper bound (blue): model
directly trained on the target data.

S | T | Info | Method | Vehicle | Person | Road | Sidewalk | Terrain | Manmade | Vegetation | mIoU
---|---|---|---|---|---|---|---|---|---|---|---
|  | Lower bound | Source | 27.16 | 6.12 | 29.19 | 8.75 | 22.87 | 51.27 | 61.47 | 29.55
nuScenes |  SemanticKITTI | Aug |  Mix3D [35] | 37.86 | 6.74 | 41.95 | 5.73 | 27.59 | 41.21 | 65.41 | 32.36
PointCutMix [62] | 55.22 | 13.92 | 45.96 | 5.43 | 30.47 | 56.50 | 70.98 | 39.78
CoSMix [39] | 44.58 | 13.88 | 36.10 | 10.19 | 29.32 | 54.43 | 69.08 | 36.80
2D DG |  IBN [36] | 22.00 | 11.32 | 37.24 | 0.21 | 13.11 | 21.76 | 50.33 | 22.28
RobustNet [7] | 32.94 | 10.98 | 39.85 | 14.70 | 28.27 | 50.42 | 58.47 | 33.66
3D UDA |  SN [50] | 25.69 | 5.46 | 19.59 | 2.17 | 23.47 | 27.65 | 61.07 | 23.58
RayCast [24] | 28.30 | 16.09 | 45.80 | 9.44 | 20.56 | 38.56 | 61.83 | 31.51
3D DG | Ours (LiDOG) | 60.07 | 9.03 | 47.44 | 16.40 | 32.58 | 54.21 | 68.82 | 41.22
Upper bound | Target | 81.57 | 23.23 | 82.09 | 57.64 | 46.84 | 64.14 | 75.21 | 61.53

Single-source.  In Tab. 1, we report the results for single-source
Synth4D-KITTI$\rightarrow$Real. We report analogous
Synth4D-nuScenes$\rightarrow$Real results with similar findings in the
appendix. We observe a $36.84$ mIoU gap (SemanticKITTI) between the source
($24.69$ mIoU) and target ($61.53$ mIoU) models. Among the baselines,
augmentation-based methods are the most effective: PointCutMix and Mix3D
achieve $42.14$ mIoU and $30.36$ mIoU on Synth4D-KITTI$\rightarrow$Real,
respectively. Surprisingly, these approaches outperform 3D DA baselines, that
leverage prior information about the target domain, _e.g_., vehicle dimensions
(SN) or target sensor specs (RayCast), confirming the efficacy of data
augmentations for DG. LiDOG significantly reduces the domain gap between
source and target models and outperforms all the compared baselines in all the
scenarios. For example, on Synth4D-KITTI$\rightarrow$Real (Tab. 1), we obtain
$44.18$ mIoU, a $+19.49$ improvement over the source model.

In Fig. 5, we discuss qualitative results, comparing the source model, the
top-performing baseline, Mix3D, our proposed LiDOG, and the ground-truth
labels. As we can see, the source model predictions are often incorrect and
mingled in many small and large regions. Mix3D brings (limited) improvements.
For example, notice how road (Fig. 5 top) is incorrectly segmented. LiDOG
achieves overall top performance, improving on both small (vehicle) and large
(building and road) areas. We report additional qualitative results in the
appendix.

Multi-source.  In Tab. 2, we report the results for the multi-source training,
where we train our model on both synthetic datasets, and evaluate the model on
both real datasets. Compared to the single source (Tab. 1), in this setting we
train models on synthetic data that mimics two different sensor types. The
source model improves from $24.69\to 31.82$ mIoU on SemanticKITTI and from
$20.62\to 25.60$ mIoU on nuScenes as a result of training on multiple
synthetic datasets. As can be seen, LiDOG consistently improves over top-
scoring baselines, RobustNet ($+3.04$ mIoU) and Mix3D ($+4.56$ mIoU). On
average, LiDOG significantly improves over the source model with $+10.62$ mIoU
and $+14.63$ mIoU on SemanticKITTI and nuScenes, respectively. We conclude
that LiDOG is consistently a top-performer for classes and scenarios over the
source model, except for the terrain class. This may be a limitation of our
BEV projection. We assume it occurs when multiple classes are spatially
overlapping due to the top-down projection, _e.g_., mixing terrain with
vegetation.

###  5.3 Real $\to$ Real Evaluation

In this setting, we only train and evaluate our models on real-world
recordings. We report SemanticKITTI$\rightarrow$nuScenes results in Tab. 3 and
nuScenes$\rightarrow$SemanticKITTI results in Tab. 4. Overall, we observe that
the performance gap of Real$\rightarrow$Real (Tab. 3-4) is lower compared to
the gap in Synth$\rightarrow$Real setting (Tab. 1-2 and Tab. 1 in the
appendix). SemanticKITTI$\rightarrow$nuScenes.  We observe a performance gap
of $21.96$ mIoU between source ($26.53$ mIoU) and target ($48.49$ mIoU)
models. All the baselines consistently improve over the source model, however,
by a smaller margin as compared to Synth$\rightarrow$Real. Again, LiDOG
improves on all the classes compared to the source performance and achieves
top generalization performance on all the classes except for vehicle and
manmade. On average, LiDOG obtains $34.88$ mIoU and obtains $+8.35$ mIoU
improvement over the source model. nuScenes$\rightarrow$SemanticKITTI.  We
observe a performance gap of $31.98$ mIoU with source and target models
achieving $29.55$ mIoU and $61.53$ mIoU, respectively. Augmentation-based
methods are once again the most effective baselines, with PointCutMix
achieving $39.78$ mIoU. LiDOG outperforms all the compared baselines, achieves
$41.22$ mIoU and obtains $+11.67$ over the source model.

\begin{overpic}[width=108.405pt]{fig/ablation/barplot_double_kitti.pdf} \end{overpic} | \begin{overpic}[width=108.405pt]{fig/ablation/barplot_double_nusc.pdf} \end{overpic}
---|---
Figure 6: Effectivenes of the BEV head: We compare 2D BEV decoder (Ours) to
simply adding an additional 3D segmentation head (Double) on SemanticKITTI
(left) and nuScenes (right). Source: $Synth4D-KITTI$.

###  5.4 Ablations

BEV auxiliary task.  We study whether LiDOG generalization capabilities are
due to the additional segmentation head, or specifically due to the dense BEV
segmentation head. The use of multiple prediction heads for ensembling is a
well-known practice for improving prediction robustness [6, 12]. To this
purpose, we remove the lower branch of LiDOG and attach an additional 3D
branch. The resulting architecture has two sparse 3D heads $h^{3D}$ taking as
input $F^{3D}$. During inference, predictions are obtained by averaging the
predictions from both heads. Fig. 6 reports the generalization performance of
this alternate version (Double) compared to LiDOG (Ours). As can be seen, by
simply learning two decoders and ensembling predictions we obtain lower
performance as compared to utilizing the proposed BEV network heads to improve
the generalization.

BEV prediction area.  First, we study the impact on the generalization ability
of LiDOG w.r.t. BEV area size in Synth4D-KITTI$\rightarrow$Real setting. We
experiment with projection bounds $B^{3D}$ ranging from $10x10$ to $60x60m$.
As can be seen in Fig. 7, there is a near-linear relation between the BEV area
and generalization performance.

Figure 7: BEV prediction area: We study the impact of BEV area size on
SemanticKITTI (top) and nuScenes (bottom). $50x50m$ area is consistently the
best-performing option on both datasets. Source: $Synth4D-KITTI$.

##  6 Conclusions

Impact.  With the advent of autonomous agents, there will be an increasing
need for robust perception models that can generalize across different
environments and sensory configurations. Robustness to domain shift in
semantic LiDAR scene understanding is therefore an important challenge that
needs to be addressed. Fully supervised fine-tuning can be used to bridge
domain gaps, however, it comes at the high cost of collecting and annotating
data for specific domains and is therefore not a scalable solution. Domain
generalization.  To the best of our knowledge, this is the first work that
investigates domain generalization techniques to learn robust and
generalizable representations for LiDAR semantic segmentation. We evaluate and
discuss a comprehensive set of baselines from related fields and study their
generalization across synthetic and real datasets, proving the need for more
effective solutions. To this end, we propose LiDOG that encourages a 3D
semantic backbone to learn features robust to domain shifts and is
consistently more accurate than the compared baselines. Limitations.  The main
limitation of LiDOG is that its core contribution, the auxiliary bird’s-eye
prediction task, may lead to overlap after the projection to the BEV plane
when classes overlap vertically, _e.g_., the terrain below vegetation. This
results in sub-optimal performance for these classes. This issue could be
mitigated with the introduction of soft labels for overlapping semantic
classes, or introducing additional losses on multiple BEV height slices
(_c.f_., [57]).

Acknowledgments.  This project was partially funded by the Sofja Kovalevskaja
Award of the Humboldt Foundation, the EU ISFP project PRECRISIS
(ISFP-2022-TFI-AG-PROTECT-02-101100539), the PRIN project LEGO-AI (Prot.
2020TA3K9N) and the MUR PNRR project FAIR - Future AI Research (PE00000013)
funded by the NextGenerationEU. It was carried out in the Vision and Learning
joint laboratory of FBK-UNITN and used the CINECA, NVIDIA-AI TC clusters to
run part of the experiments. We thank T. Meinhardt and I. Elezi for their
feedback on this manuscript. The authors of this work take full responsibility
for its content.

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Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic
Segmentation
Supplementary Material

##  Appendix A Experimental Evaluation

We provide the implementation details of our experiments in Sec. A.1. In Sec.
A.2, we complete the set of experiments of the main paper and report the
experimental results obtained on Synth4D-nuScenes$\to$Real. In Sec. A.3, we
further ablate how the input resolution affects LiDOG performance.

###  A.1 Implementation details

We implement our method and all the baselines by using the PyTorch framework.
We use MinkowskiNet [8] as a sparse convolutional backbone in all our
experiments and train until convergence with voxel size $0.05$m, total batch
size of $16$, learning rate $0.01$ and ADAM optimizer [22]. In the experiments
we use random rotation, scaling, and downsampling for better convergence of
baselines and LiDOG. We set rotation bounds between $[-\pi/2,\pi/2]$, scaling
between $[0.95,1.05]$, and perform random downsampling for 80% of the patch
points. We set projection bounds $B^{3D}$ based on the input resolution. We
set $b_{x}$ and $b_{z}$ to $50m$ in the denser source domains of Synth4D-KITTI
and SemanticKITTI and $b_{x}$ and $b_{z}$ to $30m$ in the sparser domains of
Synth4D-nuScenes and nuScenes. Quantization parameters $x_{q}$ and $z_{q}$ are
computed based on the spatial bounds in order to obtain BEV labels of
$168x168$ pixels. For downsampling dense features, we use a max pooling layer
with window size $5$, stride $3$, and padding $1$. The LiDOG 2D decoder is
implemented with a series of three 2D convolutional layers interleaved by
batch normalization layers. ReLU activation function is used on all the layers
while softmax activation is applied on the last layer.

###  A.2 Synth4D-nuScenes$\to$Real

In Tab. 5, we report the results for single-source
Synth4D-nuScenes$\rightarrow$Real. We observe a $41.82$ mIoU gap
(SemanticKITTI) between the source ($19.71$ mIoU) and target ($61.53$ mIoU)
models on Synth4D-nuScenes$\to$SemanticKITTI and a $23.92$ mIoU gap
(nuScenes), between source ($24.57$ mIoU) and target ($48.49$ mIoU) on
Synth4D-nuScenes$\to$nuScenes. Among the baselines, augmentation-based methods
are the most effective with Mix3D achieving $31.64$ mIoU (SemanticKITTI) and
$31.23$ mIoU (nuScenes). LiDOG reduces the domain gap between source and
target models and outperforms all the compared baselines in all the scenarios.
For example, on Synth4D-nuScenes$\rightarrow$Real (Tab. 5), we obtain $34.79$
mIoU, a $+15.08$ improvement over the source model.

Table 5: Synth4D-nuScenes$\rightarrow$Real, single-source. We train our model
on Synth4D-nuScenes and test on SemanticKITTI and nuScenes. LiDOG improves
over the source models by $+15.08$ mIoU on SemanticKITTI and by $+9.21$ mIoU
on nuScenes. LiDOG outperforms all the compared baselines. Lower bound (red):
a model trained n the source domain without the help of DG techniques. Upper
bound (blue): a model directly trained on target data.

S | T | Info | Method | Vehicle | Person | Road | Sidewalk | Terrain | Manmade | Vegetation | mIoU
---|---|---|---|---|---|---|---|---|---|---|---
Synth4D-nuScenes |  SemanticKITTI | Lower bound | Source | 14.54 | 2.41 | 32.78 | 14.86 | 6.4 | 30.89 | 36.07 | 19.71
Aug |  Mix3D [35] | 37.38 | 7.26 | 56.90 | 21.06 | 10.60 | 34.46 | 53.79 | 31.64
PointCutMix [62] | 27.51 | 4.32 | 56.04 | 21.37 | 7.02 | 24.38 | 45.35 | 26.57
CoSMix [39] | 16.13 | 6.42 | 39.71 | 14.63 | 13.04 | 23.54 | 30.57 | 20.58
2D DG |  IBN [36] | 47.15 | 7.81 | 50.74 | 4.51 | 15.20 | 29.53 | 51.35 | 29.47
RobustNet [7] | 21.19 | 8.56 | 44.46 | 10.80 | 15.06 | 11.95 | 30.59 | 20.37
3D UDA |  SN [50] | 11.56 | 2.38 | 37.50 | 10.86 | 5.19 | 20.70 | 39.23 | 18.20
RayCast [24] | 28,89 | 6.34 | 53.59 | 12.94 | 15.86 | 21.74 | 41.85 | 25.89
3D DG | Ours (LiDOG) | 55.08 | 11.42 | 59.48 | 26.10 | 2.78 | 34.83 | 53.82 | 34.79
Upper bound | Target | 81.57 | 23.23 | 82.09 | 57.64 | 46.84 | 64.14 | 75.21 | 61.53
Synth4D-nuScenes |  nuScenes | Lower bound | Source | 17.73 | 8.94 | 36.53 | 7.28 | 5.78 | 52.13 | 43.62 | 24.57
Aug |  Mix3D [35] | 33.83 | 17.85 | 47.74 | 8.93 | 9.71 | 56.35 | 44.18 | 31.23
PointCutMix [62] | 21.51 | 13.12 | 53.72 | 8.86 | 8.45 | 54.83 | 48.81 | 29.90
CoSMix [39] | 22.00 | 15.43 | 57.40 | 8.86 | 9.08 | 56.24 | 47.16 | 30.88
2D DG |  IBN [36] | 23.07 | 14.13 | 44.96 | 7.10 | 9.83 | 53.70 | 49.49 | 28.90
RobustNet [7] | 21.59 | 12.29 | 48.52 | 8.14 | 6.22 | 51.33 | 47.68 | 27.97
3D UDA |  SN [50] | 24.71 | 8.45 | 5.,08 | 5.66 | 11.04 | 47.00 | 39.05 | 26.57
RayCast [24] | 19.65 | 12.24 | 58.08 | 7.58 | 9.71 | 46.43 | 41.37 | 27.86
3D DG | Ours (LiDOG) | 26.79 | 18.68 | 63.28 | 15.81 | 6.57 | 58.8 | 46.5 | 33.78
Upper bound | Target | 47.42 | 20.18 | 80.89 | 37.02 | 34.99 | 64.27 | 54.67 | 48.49

###  A.3 BEV resolution

\begin{overpic}[width=108.405pt]{fig/ablation/barplot_resolution_kitti.pdf} \end{overpic} | \begin{overpic}[width=108.405pt]{fig/ablation/barplot_resolution_nusc.pdf} \end{overpic}
---|---
Figure 8: BEV image resolution: We compare the performance while changing the
BEV image resolution on SemanticKITTI (left) and nuScenes (right), Source:
Synth4D-KITTI

We study the impact of BEV image resolution on the LiDOG performance. We adapt
the feature resolution by applying several pooling steps and the label
resolution via the quantization step size. In Fig. 8, we report the results
obtained on Synth4D-KITTI$\rightarrow$Real, when using BEV features with
$50\%$ and $75\%$ of the initial resolution ($100\%$). Interestingly, on
Synth4D-KITTI$\rightarrow$nuScenes we observe a slight improvement with $75\%$
resolution. However, we obtain consistently top performance on both domains
with the full resolution ($100\%$).

##  Appendix B Qualitative evaluation

We report additional qualitative results for each baseline and in all the
studied generalization directions. In Fig. 9-11 we show qualitative results in
the Synth$\to$Real setting: Synth4D-kitti$\to$Real (Fig. 9),
Synth4D-nuScenes$\to$Real (Fig. 10) and
Synth4D-kitti$+$Synth4D-nuScenes$\to$Real (Fig. 11). In Fig. 12-13 we show
qualitative results in the SemanticKITTI$\to$nuScenes and
nuScenes$\to$SemanticKITTI directions, respectively. Source predictions are
often incorrect and spatially inconsistent. Baselines consistently improve
over the source model performance. LiDOG achieves the overall best performance
with improved and more precise predictions. This can be seen in all the
reported results, both synth$\to$real and real$\to$real.

\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2kitti/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2kitti/source_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2nusc/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2nusc/source_1.jpg} \end{overpic}
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\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2kitti/lidog_0.jpg} \put(-4.5,500.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{source}}} \put(-6.0,440.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{mix3D}}} \put(-6.0,375.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{p.cutmix}}} \put(-6.0,317.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{cosmix}}} \put(-6.0,260.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ibn}}} \put(-6.0,200.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{robust.}}} \put(-5.0,140.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{sn}}} \put(-6.0,75.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{raycast}}} \put(-5.0,17.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ours}}} \put(-6.0,-35.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{gt}}} \put(80.0,541.0){\color[rgb]{0,0,0}\footnotesize{SemanticKITTI}} \put(290.0,541.0){\color[rgb]{0,0,0}\footnotesize{nuScenes}} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2kitti/lidog_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2nusc/lidog_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dkitti2nusc/lidog_1.jpg} \end{overpic}
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Figure 9: Qualitative results. Top: Synth4D-kitti$\rightarrow$SemanticKITTI, bottom: Synth4D-kitti$\rightarrow$nuScenes. LiDOG improves over source and baselines, _e.g_., we observe the improvements of road in SemanticKITTI and vehicle in nuScenes. \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/source_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/source_1.jpg} \end{overpic}
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\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/ray_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/ray_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/lidog_0.jpg} \put(-4.5,500.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{source}}} \put(-6.0,440.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{mix3D}}} \put(-6.0,375.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{p.cutmix}}} \put(-6.0,317.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{cosmix}}} \put(-6.0,260.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ibn}}} \put(-6.0,200.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{robust.}}} \put(-5.0,140.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{sn}}} \put(-6.0,75.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{raycast}}} \put(-5.0,17.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ours}}} \put(-6.0,-35.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{gt}}} \put(80.0,541.0){\color[rgb]{0,0,0}\footnotesize{SemanticKITTI}} \put(290.0,541.0){\color[rgb]{0,0,0}\footnotesize{nuScenes}} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/lidog_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/lidog_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/lidog_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2kitti/gt_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/synth4dnusc2nusc/gt_1.jpg} \end{overpic}
Figure 10: Qualitative results. Left: Synth4D-nuScenes$\rightarrow$SemanticKITTI, right: Synth4D-nuScenes$\rightarrow$nuScenes. LiDOG improves over source and baselines, _e.g_., we observe the improvements of road in SemanticKITTI and manmade in nuScenes. \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/source_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/source_1.jpg} \end{overpic}
---|---|---|---
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/mix3d_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/mix3d_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/mix3d_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/mix3d_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/point_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/point_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/point_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/point_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/cosmix_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/cosmix_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/cosmix_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/cosmix_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/ibn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/ibn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/ibn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/ibn_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/robust_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/robust_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/robust_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/robust_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/sn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/sn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/sn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/sn_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/ray_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/ray_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/lidog_0.jpg} \put(-4.5,500.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{source}}} \put(-6.0,440.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{mix3D}}} \put(-6.0,375.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{p.cutmix}}} \put(-6.0,317.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{cosmix}}} \put(-6.0,260.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ibn}}} \put(-6.0,200.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{robust.}}} \put(-5.0,140.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{sn}}} \put(-6.0,75.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{raycast}}} \put(-5.0,17.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ours}}} \put(-6.0,-35.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{gt}}} \put(80.0,541.0){\color[rgb]{0,0,0}\footnotesize{SemanticKITTI}} \put(290.0,541.0){\color[rgb]{0,0,0}\footnotesize{nuScenes}} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/lidog_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/lidog_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/lidog_1.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2kitti/gt_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/multi2nusc/gt_1.jpg} \end{overpic}
Figure 11: Qualitative results. Top: Synth4D-kitti$+$Synth4D-nuScenes$\to$SemanticKITTI, bottom: Synth4D-kitti$+$Synth4D-nuScenes$\to$nuScenes. LiDOG improves over source and baselines, _e.g_., we observe the improvements of vegetation in SemanticKITTI and road in nuScenes. \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/source_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/source_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/source_3.jpg} \end{overpic}
---|---|---|---
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/mix3d_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/mix3d_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/mix3d_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/mix3d_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/point_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/point_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/point_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/point_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/cosmix_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/cosmix_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/cosmix_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/cosmix_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ibn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ibn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ibn_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ibn_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/robust_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/robust_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/robust_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/robust_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/sn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/sn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/sn_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/sn_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ray_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ray_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/ray_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/lidog_0.jpg} \put(-4.5,500.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{source}}} \put(-6.0,440.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{mix3D}}} \put(-6.0,375.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{p.cutmix}}} \put(-6.0,317.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{cosmix}}} \put(-6.0,260.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ibn}}} \put(-6.0,200.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{robust.}}} \put(-5.0,140.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{sn}}} \put(-6.0,75.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{raycast}}} \put(-5.0,17.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ours}}} \put(-6.0,-35.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{gt}}} \put(185.0,542.0){\color[rgb]{0,0,0}\footnotesize{nuScenes}} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/lidog_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/lidog_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/lidog_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/gt_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/gt_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/kitti2nusc/gt_3.jpg} \end{overpic}
Figure 12: Qualitative results. Top: SemanticKITTI$\to$nuScenes. LiDOG improves over source and baselines, _e.g_., we observe the improvements in terrain, road, and manmade. \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/source_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/source_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/source_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/source_3.jpg} \end{overpic}
---|---|---|---
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/mix3d_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/mix3d_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/mix3d_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/mix3d_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/point_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/point_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/point_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/point_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/cosmix_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/cosmix_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/cosmix_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/cosmix_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ibn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ibn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ibn_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ibn_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/robust_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/robust_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/robust_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/robust_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/sn_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/sn_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/sn_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/sn_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ray_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ray_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ray_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/ray_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/lidog_0.jpg} \put(-4.5,500.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{source}}} \put(-6.0,440.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{mix3D}}} \put(-6.0,375.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{p.cutmix}}} \put(-6.0,317.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{cosmix}}} \put(-6.0,260.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ibn}}} \put(-6.0,200.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{robust.}}} \put(-5.0,140.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{sn}}} \put(-6.0,75.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{raycast}}} \put(-5.0,17.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{ours}}} \put(-6.0,-35.0){\rotatebox{90.0}{\color[rgb]{0,0,0}\footnotesize{gt}}} \put(185.0,542.0){\color[rgb]{0,0,0}\footnotesize{SemanticKITTI}} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/lidog_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/lidog_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/lidog_3.jpg} \end{overpic}
\begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/gt_0.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/gt_1.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/gt_2.jpg} \end{overpic} | \begin{overpic}[width=104.34314pt]{fig/qualitative/supplementary/nusc2kitti/gt_3.jpg} \end{overpic}
Figure 13: Qualitative results. Top: nuScenes$\to$SemanticKITTI. LiDOG
improves over source and baselines, _e.g_., we observe the improvements in
sidewalk and road.