# Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation
for 3D Object Detection

Yecheol Kim1∗, Junho Lee1∗, Changsoo Park2, Hyoung won Kim2, Inho Lim2,
Christopher Chang2,
and Jun Won Choi3
1Y. Kim and J. Lee are with Department of Electrical Engineering, Hanyang
University, 04753 Seoul, Republic of Korea. (e-mail<EMAIL_ADDRESS>jhlee@spa.hanyang.ac.kr)2C. Park, H. Kim, I. Lim and C. Chang are with Kakao
Mobility Corp, Pangyo 13529, Republic of Korea. (e-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>cswchang@alumni.caltech.edu)3J. W. Choi is with
Department of Electrical and Computer Engineering, College of Liberal Studies,
Seoul National University, Seoul, 08826, Korea. (e-mail:
junwchoi@snu.ac.kr)(Corresponding author: Jun Won Choi)∗denotes equal
contribution.

###### Abstract

3D object detection is crucial for applications like autonomous driving and
robotics. However, in real-world environments, variations in sensor data
distribution due to sensor upgrades, weather changes, and geographic
differences can adversely affect detection performance. Semi-Supervised Domain
Adaptation (SSDA) aims to mitigate these challenges by transferring knowledge
from a source domain, abundant in labeled data, to a target domain where
labels are scarce. This paper presents a new SSDA method referred to as
Target-Oriented Domain Augmentation (TODA) specifically tailored for LiDAR-
based 3D object detection. TODA efficiently utilizes all available data,
including labeled data in the source domain, and both labeled data and
unlabeled data in the target domain to enhance domain adaptation performance.
TODA consists of two stages: TargetMix and AdvMix. TargetMix employs mixing
augmentation accounting for LiDAR sensor characteristics to facilitate feature
alignment between the source-domain and target-domain. AdvMix applies point-
wise adversarial augmentation with mixing augmentation, which perturbs the
unlabeled data to align the features within both labeled and unlabeled data in
the target domain. Our experiments conducted on the challenging domain
adaptation tasks demonstrate that TODA outperforms existing domain adaptation
techniques designed for 3D object detection by significant margins. The code
is available at: https://github.com/rasd3/TODA.

###### Index Terms:

Autonomous driving, 3D object detection, semi-supervised domain adaptation.

††publicationid: pubid: 0000–0000/00$00.00 © 2024 IEEE

##  I Introduction

3D object detection is the task of detecting and localizing objects in 3D
world coordinates using sensor measurements. 3D object detection has risen as
a pivotal perception task in the field of autonomous vehicles and robotics.
Recently, 3D point cloud data acquired by LiDAR sensor has been successfully
used to achieve promising performance in 3D object detection. The recent
progress of deep learning has sparked the development of a plethora of
architectures for detecting objects from LiDAR point cloud. Widely used 3D
object detectors include VoxelNet[1], PointPillar [2], SECOND [3], CenterPoint
[4], PV-RCNN [5], PillarNet [6], and Voxel R-CNN [7].

Figure 1: Performance evaluation in a domain adaptation task from Waymo
dataset to nuScenes dataset: 0.5%, 1%, and 5% labeled data in the target
domain are used. A SSDA method using only 0.5% of the target label results in
a remarkable performance gain over a UDA method (ST3D [8]). Our TODA also
significantly outperforms SSDA3D [9] in all settings. Surprisingly, TODA even
surpasses the Oracle performance with only 5% labels.

A shift in the distribution of data often leads to notable decreases in the
performance of 3D object detection [10, 11]. In the context of autonomous
driving, shifts in distribution arise from change in sensor suites,
fluctuations in weather conditions, disparities in geographical locations, and
more. For instance, upgrades in sensor specifications, including resolution,
field of view (FOV), and intensity, introduce shifts in data distribution. In
this case, it is inefficient to collect new training data and retrain the
model from scratch with each sensor replacement. Therefore, addressing the
domain shift problem in 3D object detection is crucial for the commercial
deployment of autonomous driving and still remains a significant open
challenge.

Domain adaptation offers a solution to this problem by allowing models trained
in source domains to adapt effectively to different but related target
domains, thereby reducing the need for extensive data labeling. Domain
adaptation strategies can be categorized into two main types: unsupervised
domain adaptation (UDA) and semi-supervised domain adaptation (SSDA).

When labeled data is unavailable within the target domain, UDA transfers
knowledge learned from labeled source domains to enhance performance in target
domains. Recent UDA approaches for 3D object detection include CL3D [12], ST3D
[8], and ST3D++ [13]. These methods have employed various domain-adaptive
pseudo-labeling approaches to mitigate domain discrepancies.

While UDA can mitigate domain shift issues, addressing significant domain gaps
between source and target domains remains challenging. While UDA is effective
in narrowing the performance gap from the Oracle111The Oracle model denotes
the fully-supervised model trained on the target domain. by approximately 80%
when applied to similar LiDAR specifications from the Waymo dataset [14] to
the KITTI dataset [15], it achieves only a 3% reduction in the gap when
dealing with markedly different LiDAR configurations, such as those from the
Waymo dataset to the nuScenes dataset [16]. To address this limitation, semi-
supervised domain adaptation (SSDA) has emerged as a cost-effective way to
improve the effect of domain adaptation. Unlike UDA, SSDA uses a small amount
of labeled target-domain data along with a substantial volume of unlabeled
target-domain data to improve performance of domain adaptation. Fig. 1
illustrates that leveraging a small amount of labeled target-domain data, SSDA
methods can yield substantial performance improvements over UDA.

SSDA3D [9], as of now, is the sole existing SSDA method specifically designed
for 3D object detection. This method operates in a two-stage process.
Initially, it incorporates an Inter-domain Point-Cutmix operation to reduce
domain bias and thus learns domain-invariant representations. Following this,
SSDA3D employs an Intra-domain Point-MixUp operation, which combines both
labeled data and pseudo-labeled scenes on a global scale under a semi-
supervised learning (SSL) framework. Despite its notable performance gains
over UDA methods, we claim that SSDA3D has not fully exploited the distinct
properties inherent in LiDAR point cloud data.

In this study, we present a novel SSDA framework referred to as Target-
Oriented Domain Augmentation (TODA) for 3D object detection. TODA employs a
two-stage data augmentation strategy for SSDA; Target Sensor-Guided Mix
Augmentation (TargetMix) and Adversarial-Guided Mix Augmentation (AdvMix).

TargetMix reduces the disparity between the source and target domains by
employing a cross-domain mixup strategy. Initially, TargetMix aligns the
characteristics of LiDAR point clouds, such as Field of View (FOV) and beam
configurations in the source-domain data with those in the target-domain data.
Subsequently, a cross-domain mixup augmentation is applied in a polar
coordinate system, incorporating an effective LiDAR distribution matching
process that considers the scanning mechanism of LiDAR technology. By
generating convex combinations of LiDAR point clouds from both source and
target domains, TargetMix ensures smooth transitions between these domains.

TargetMix does not utilize the unlabeled data in the target domain, leaving
room for further improvement. AdvMix employs a pseudo-labeling approach to
leverage this unlabeled data. However, as pointed out in [17], the pseudo-
labeling approach might suffer from intra-domain discrepancy, which arises
when the teacher model, trained solely with labeled data, yields feature
points divided into those attracted into the source domain and those that do
not. Such inconsistency within the target domain data diminishes the
effectiveness of pseudo labeling. To address this issue, we introduce AdvMix,
a technique that integrates adversarial point augmentation with mixup
augmentation. Adversarial Point Augmentation, derived from Adversarial
Augmentation in [18], involves perturbing unlabeled data at the point level.
These perturbations are informed by the negative gradient of the detection
loss calculated in terms of the unlabeled points, effectively altering their
distribution to enhance detection performance. This process generates
perturbed pseudo-labeled samples that help minimize intra-domain
discrepancies. Following this, we implement mixup augmentation, blending the
labeled data with the pseudo-labeled data on a global scale.

We evaluate the performance of TODA on the challenging domain adapation tasks:
from the Waymo dataset [14] to the nuScenes dataset [16] and from the nuScenes
dataset to the KITTI dataset [15]. Our results demonstrate that TODA yields
substantial performance improvements compared to the baseline method.
Moreover, TODA outperforms existing domain adaptation techniques, including
SSDA3D, by considerable margins.

The key contributions of TODA are summarized as follows:

  * •

We propose a novel two-stage SSDA framework for 3D object detection.

  * •

We present two novel data augmentation techniques, TargetMix and AdvMix,
designed to improve the effectiveness of domain adaptation. TargetMix aims to
reduce the domain disparity by leveraging labeled data from both the source
and target domains. In contrast, AdvMix uses a combination of labeled and
unlabeled data within the target domain to minimize the intra-domain gap.

  * •

TargetMix method is specifically designed to take into account the unique
characteristics of LiDAR point cloud data, thereby maximizing the impact of
data augmentation.

  * •

AdvMix generates adversarial examples to align the representation of unlabeled
data effectively within the target domain. In our study, we are the first to
pioneer the use of adversarial augmentation for point cloud-based SSDA.

  * •

The proposed TODA achieves state-of-the-art performance on popular domain
adaptation benchmarks. It attains performances on par with the Oracle
performance even when utilizing merely 5% of labeled data in the target
domain.

##  II Related Work

###  II-A LiDAR-based 3D Object Detection

The advancement of deep learning has led to the development of various LiDAR-
based 3D object detectors. These detectors utilize point encoding to extract
high-level semantic features. Point encoding techniques can generally be
categorized into two types: clustering-based and grid-based methods.
Clustering-based methods group point clouds into clusters and encode points
within each cluster in a hierarchical manner. Examples of clustering-based
methods include PointRCNN [19], 3DDS [20], and PV-RCNN [5]. In contrast, grid-
based encoding methods divide 3D point clouds into voxel or pillar grids,
thererby producing features in a structured grid pattern. 3D object detectors
that use grid-based encoding include VoxelNet [1], SECOND [3], PointPillar
[2], and CenterPoint [4].

Figure 2: Overall architecture of the proposed TODA: First, TargetMix aligns
the source-domain data with target-domain data by applying LiDAR Distribution
Matching, followed by mixup augmentation in polar coordinates. Then, AdvMix
utilizes Adversarial Point Augmentation to perturb the unlabeled data in the
target domain, aiming to produce consistent representation of both labeled and
unlabeled data. ’P’, ’A’, and ’M’ denote Polar Coordinate-based Mix,
Adversarial Point Augmentation, and Point-Mixup respectively.

###  II-B Domain Adaptation for 3D Object Detection

Several UDA methods have proposed for 3D object detection. PointDAN [21] and
SRDAN [22] transformed object features taking factors like the range and
distance of point clouds into account. ST3D [8] and ST3D++ [13] tackled the
challenge of learning instability induced by domain shift through the
utilization of a memory bank and denoising techniques. Currently, only one
SSDA method available in the literature is SSDA3D [9]. SSDA3D employed mixing
augmentation strategies to reduce the distribution gap between the source and
target domains.

###  II-C Mixing Augmentation for Domain Adaptation

Mixing augmentation has emerged as an effective strategy for addressing data
limitations and enhancing robustness in semi-supervised learning (SSL) and
domain adaptation. Mixup [23] generated new training data by creating convex
combinations of input pairs, subsequently training the model on these blended
inputs and their corresponding labels. CutMix [24], a variant of Mixup for
image recognition, constructed new images by randomly cutting a rectangular
patch from one image and pasting it onto another at a random location. These
methods encouraged the model to learn interpolated decision boundaries between
samples and hence improved generalization performance significantly.

In domain adaptation, mix augmentation techniques are employed to bridge
inter-domain gaps by integrating data from both the source and target domains,
thereby facilitating knowledge transfer from the source to the target domain.
PolarMix [25] introduces a specialized mix augmentation strategy for point
cloud data that combines scene-level and object-level features in cylindrical
coordinates.

###  II-D Adversarial Augmentation

Adversarial augmentation is a method for generating adversarial examples that
are intended to challenge the accuracy of a model. Recently, AT [26] and VAT
[27] have utilized a gradient-based perturbation generation method to enhance
the robustness of models in both supervised and semi-supervised tasks.
Furthermore, several studies [28, 29, 30, 31] have explored using adversarial
augmentation strategies to generate and translate point clouds.

###  II-E Semi-Supervised Learning for 3D Object Detection

SSL has also been actively studied for 3D object detection. SSL involves using
a combination of a small amount of labeled data and a large amount of
unlabeled data for training models. SESS [32] introduced a Consistency Loss as
a means to align predicted 3D object proposals between the teacher and student
networks. 3DIoUMatch [33] filtered low-quality pseudo-labels using the
combination of confidence thresholds and 3D IoU predictions. Proficient
Teachers [34] enhanced the precision of pseudo-labels through an augmented
prediction approach that incorporates box voting-based ensembling.

##  III Method

In this section, we present the details of the proposed TODA method.

###  III-A Overview

In the SSDA framework, we train the model using labeled data from the source
domain, as well as both labeled and unlabeled data from the target domain.
First, the $N_{S}$ labeled point cloud samples in the source domain are
denoted as $D_{S}=\\{p^{i}_{s},l^{i}_{s}\\}^{N_{S}}_{i=1}$, where $p^{i}_{s}$
and $l^{i}_{s}$ are the $i$th point clouds and the corresponding 3D object
detection labels. Similarly, the $N_{TL}$ labeled samples in the target domain
are denoted as $D_{TL}=\\{p_{tl}^{i},l_{tl}^{i}\\}^{N_{TL}}_{i=1}$ and the
$N_{TU}$ unlabeled samples in the target domain are denoted as
$D_{TU}=\\{p_{tu}^{i}\\}^{N_{TU}}_{i=1}$. The $i$th point clouds
$p^{i}\in\mathbb{R}^{N_{p}\times 4}$ contain LiDAR points where each point
measurement includes 3D coordinates $(x,y,z)$, and intensity $I$. The
corresponding set of labels, $l^{i}$ contains descriptions of 3D object boxes,
consisting of category, location, size, and heading angle. In typical SSDA
setup, we assume that both $N_{S}$ and $N_{TU}$ are significantly larger than
$N_{TL}$, i.e. $N_{S}>>N_{TL}$ and $N_{TU}>>N_{TL}$. The objective of SSDA is
to maximize the object detection performance in the target domain through an
effective use of $D_{S}$, $D_{TL}$, and $D_{TU}$.

Fig. 2 depicts the two-stage structure of the proposed TODA framework. In the
first stage, TargetMix initially performs LiDAR Distribution Matching, which
transforms the source-domain LiDAR data $D_{S}$ into the LiDAR data
$D^{\prime}_{S}$ such that the transformed LiDAR data $D^{\prime}_{S}$ follows
the configurations of the target domain LiDAR. Subsequently, TargetMix
combines the transformed data $D^{\prime}_{S}$ with $D_{TL}$ in polar
coordinates using a weighted mixup operation as proposed in [24]. This
operation results in a mixed dataset
$D_{TM}=\\{p_{tm}^{i},l_{tm}^{i}\\}^{N_{TM}}_{i=1}$. The mixed data $D_{TM}$
is then utilized to train a 3D object detection model, aimed at bridging the
gap between the two domains.

In the second stage, we utilize two 3D object detection pipelines with
identical structures. As illustrated in Fig. 2, one model $f_{t}$ serves as a
teacher, while the other $f_{s}$ functions as a student model. The weights of
the model trained in the first stage are initially copied to the teacher
model. The teacher model is then employed to generate pseudo-labels
$\\{\hat{l}^{i}_{tu}\\}^{N_{TU}}_{i=1}$ from the unlabeled data $D_{TU}$. At
the same time, the corresponding unlabeled data $D_{TU}$ are perturbed by the
Adversarial Point Augmentation. These perturbed samples are then mixed with
the labeled data $D_{TL}$, resulting in the mixed data
$D_{AM}=\\{p_{am}^{i},\hat{l}_{tu}^{i}\\}^{N_{TU}}_{i=1}$. Finally, the
student model is trained using the mixed data
$D_{AM}=\\{p_{am}^{i},\hat{l}_{tu}^{i}\\}^{N_{TU}}_{i=1}$. By incorporating
adversarial examples, TODA ensures a consistent representation of the
unlabeled samples to train the student model, facilitating the effective
utilization of unlabeled target-domain samples.

Figure 3: Comparison of TargetMix with PolarMix: TargetMix divides the entire
azimuth angle into $2K$ separate sectors while PolarMix divides it into two
sectors.

###  III-B Target Sensor-Guided Mix

TargetMix combines LiDAR points from source and target domains. TargetMix
performs two steps. The first step converts the source-domain LiDAR data
$D_{S}$ into the data
$D^{\prime}_{S}=\\{\tilde{p}^{i}_{s},l^{i}_{s}\\}^{N_{S}}_{i=1}$. This LiDAR
Distribution Matching step adjusts the number of beam channels, the number of
points per channel, and Vertical Field of View (VFOV) of $D_{S}$ to match the
corresponding parameters of the LiDAR data in the target domain. Specifically,
TargetMix converts the 3D points in $D_{S}$ from Cartesian coordinates
$(x,y,z)$ to spherical coordinates:

$\displaystyle\theta=\arctan\frac{z}{\sqrt{x^{2}+y^{2}}},\quad\phi=\arcsin\frac{y}{\sqrt{x^{2}+y^{2}}},$
(1)

where $\theta$ and $\phi$ denote the azimuth and zenith angles, respectively.
Using the point data in spherical coordinates, we then generate the range
image $I_{r}\in\mathbb{R}^{H\times W}$, where each pixel in $I_{r}$
corresponds to specific $\theta$ and $\phi$ values. The image $I_{r}$ is
downsampled based on the ratio of the number of beam channels, the number of
points per channel, and the VFOV. The downsampled image $I^{\prime}_{r}$ is
transformed back into the Cartesian coordinate system to generate the
transformed source data $D^{\prime}_{S}$. For instance, consider the Waymo
dataset as source-domain data and the nuScenes dataset as target-domain data.
LiDAR sensor used in Waymo dataset has 64 channels, about 2200 points per
channel, and a VFOV of $[-17.6^{\circ},2.4^{\circ}]$, covering a range of
$20^{\circ}$. On the other hand, LiDAR sensor in nuScenes dataset has 32
channels, 1100 points per channel, and a VFOV of
$[-30.0^{\circ},10.0^{\circ}]$, covering a range of $40^{\circ}$. The image
$I_{r}$ is downsized vertically by a factor of $4$, considering both the VFOV
range ratio $\frac{40^{\circ}}{20^{\circ}}$ and the channel ratio
$\frac{64}{32}$. Additionally, the image is downsized horizontally by a factor
of $2$ to match the points per channel between the two datasets.

The second step of TargetMix applies mix augmentation between $D^{\prime}_{S}$
and $D_{TL}$ in the polar coordinate system, where each LiDAR point is
characterized by $(\theta,r,\phi)$: $\theta$ represents the azimuth angle, $r$
denotes the distance, and $\phi$ indicates the inclination angle between the
z-axis and the point vector $(x,y,z)$. Inspired by PolarMix [25], TargetMix
conducts a mix operation by partitioning the azimuth angle into two sections
and filling one with data points from $D^{\prime}_{S}$ and the other with
those from $D_{TL}$. This mix operation is also applied to 3D box labels
accordingly. Unlike PolarMix, which utilizes a single contiguous sector for
$D_{TL}$, TargetMix assigns $K$ separate contiguous sectors to $D_{TL}$,
accounting for the widespread distribution of objects. The remaining regions
are assigned to $D^{\prime}_{S}$. However, such partitioning can lead to
ambiguity, as LiDAR points within objects may be divided by azimuth
boundaries. To mitigate this issue, TargetMix not only eliminates the object
boxes affected by these boundaries but also removes their corresponding point
cloud data. This operation is referred to as Enhanced Mix Strategy. We denote
the data generated by Enhanced Mix Strategy as
$D_{TM}=\\{p_{tm}^{i},l_{tm}^{i}\\}_{i=1}^{N_{TM}}$. Note that

$\displaystyle p_{tm}^{i}=(M_{p}\odot\tilde{p}^{i}_{s})\oplus((1-M_{p})\odot
p^{j}_{tl})$ (2) $\displaystyle l_{tm}^{i}=(M_{l}\odot
l^{i}_{s})\oplus((1-M_{l})\odot l^{j}_{tl})$ (3)

where $M_{p},M_{l}$ denotes a binary mask list indicating the $K$ azimuth
ranges $[[\alpha^{1},\beta^{1}],...[\alpha^{K},\beta^{K}]]$, $\odot$ is the
element-wise multiplication operation, and $\oplus$ denotes the concatenation
operation. TargetMix randomly selects either a mixed sample from $D_{TM}$ or a
sample from $D^{\prime}_{S}$ with a probability $P_{tm}$, and the chosen
sample is fed into the 3D object detection model for training.

###  III-C Adversarial-Guided Mix

The teacher model $f_{t}$ is initialized with the model trained by TargetMix
in the first stage. Then, the teacher model $f_{t}$ generates pseudo labels
$\\{\hat{l}^{i}_{tu}\\}^{N_{TU}}_{i=1}$ from the unlabeled target data
$D_{TU}$. However, because the teacher model is trained on a limited subset of
labeled data in both the source and target domains, it may not fully capture
the distribution of the unlabeled target data. This results in intra-domain
discrepancy, where the feature points from labeled data exhibit a different
distribution from those of the unlabeled data. Since the teacher model is
trained with labeled data, some unlabeled data may also be influenced by the
labeled data while others are not. This discrepancy is evident in Fig. 4 (a),
where a noticeable distribution gap exists between the representation of the
labeled data and that of the unlabeled data. Such intra-domain inconsistency
presents challenges when training the student model using the inconsistently
represented labeled and unlabeled data.

To address this issue, we employ adversarial augmentation as proposed in [18].
This technique aligns data distribution with the target distribution by
perturbing input data in the direction of the negative gradient of the loss
function derived from a model trained on the target distribution. AdvMix
specifically implements Adversarial Point Augmentation on unlabeled LiDAR data
within the target domain, altering the positions of LiDAR points in 3D space
to optimize domain adaptation performance. More precisely, LiDAR points
located within detection boxes provided by pseudo labels, are subject to
perturbation. Within each bounding box, a subset of LiDAR points is randomly
chosen to be perturbed, based on a probability $\rho$. Subsequently, for each
selected point, one of three types of adversarial perturbations is applied

  * •

Point translation: a perturbation $\delta$ is added to the $(x,y,z)$
coordinates of the selected point

  * •

Point addition: a new point is generated by translating the coordinates of the
selected point by $\delta$

  * •

Point removal: the selected point is removed.

One of the three types is randomly chosen with equal probability, producing
the adversarial samples $D_{adv}=\\{p_{adv}^{i},l_{adv}^{i}\\}_{i=1}^{N_{TU}}$
The direction of the perturbation $\delta$ is determined by the negative
gradient of the detection loss. For the point cloud input $p_{tu}$, the
perturbation $\delta$ can be calculated as

$\displaystyle\delta$ $\displaystyle=\epsilon\frac{g}{||g||_{2}},$ (4)
$\displaystyle g$
$\displaystyle=-\nabla_{p_{tu}}\mathcal{L}_{det}(f_{t}(p_{tu}),\hat{l}_{tu}),$
(5)

where $\mathcal{L}_{det}$ is the detection loss comprising the classification
and regression losses, $\hat{l}_{tu}$ includes the pseudo labels obtained from
$p_{tu}$, $g\in\mathbb{R}^{3}$ is the gradient of the detection loss with
respect to the $(x,y,z)$ coordinates of the points in $p_{tu}$, and $\epsilon$
denotes the magnitude of the perturbation. This gradient indicates the
direction of perturbation that reduces the detection loss. Fig. 4 (b) shows
that the Adversarial Point Augmentation results in the reduced gap in
distribution between the labeled and unlabeled data. Consequently, this can
foster a more consistent and aligned learning process for the student model.

Algorithm 1 Target Oriented Data Augmentation (TODA)

Input: Source data $(p_{s},l_{s})\in D_{S}$, Target labeled data
$(p_{tl},l_{tl})\in D_{TL}$, Target unlabeled data $p_{tu}\in D_{TU}$, Teacher
model $f_{t}$, Student model $f_{s}$, the probability of TargetMix $P_{tm}$,
the probability of AdvMix $P_{am}$, Regularization parameter $\lambda$, total
epoch of TargetMix stage $T_{\text{TM}}$, total epoch of AdvMix stage
$T_{\text{AM}}$

1:Apply DataTransformation: $D^{\prime}_{s}$ $\triangleright$ TargetMix stage

2:for $T=1$ to $T_{\text{TM}}$ do

3: for $i=1$ to $N_{S}+N_{TL}$ do

4: if rand() $<P_{tm}$ then

5: Sample $(p^{\prime i}_{s},l^{i}_{s})$ and $(p^{i}_{tl},l^{i}_{tl})$

6: Apply PolarEnhanceMix: ($p^{\prime i}_{tm}$, $l^{i}_{tm}$)

7: else

8: Sample $(p^{\prime i}_{s},l^{i}_{s})$ if $i<N_{S}$ else
$(p^{i}_{tl},l^{i}_{tl})$

9: end if

10: Calculate $\mathcal{L}_{det}$ and update $f_{t}$

11: end for

12:end for

13:Initialize $f_{s}$ with $f_{t}$ pre-trained weights $\triangleright$ AdvMix
stage

14:Generate pseudo labels $\hat{l}_{tu}$ using $f_{t}$

15:for $T=1$ to $T_{\text{AM}}$ do

16: for $i=1$ to $N_{TL}+N_{TU}$ do

17: Sample $(p^{i}_{tl},l^{i}_{tl})$ and $(p^{i}_{tu},\hat{l}^{i}_{tu})$

18: Generate adversarial example $p^{i}_{adv}$

19: if rand() $<P_{am}$ then

20: Apply PointMixUp: ($\tilde{p}^{i}_{AM}$, $\hat{l}^{i}_{AM}$),
($\tilde{p}^{i}_{PM}$, $\hat{l}^{i}_{PM}$)

21: Calculate
$\mathcal{L}_{cons}(f_{s}(\tilde{p}^{i}_{AM}),f_{s}(\tilde{p}^{i}_{PM}))$

22: else

23: Calculate $\mathcal{L}_{cons}(f_{s}(p^{i}_{tu}),f_{s}(p^{i}_{adv}))$

24: end if

25: Calculate $\mathcal{L}_{det}$ and update $f_{s}$

26: end for

27:end for

Finally, we mix the perturbed unlabeled target data $D_{adv}$ and the labeled
target data $D_{TL}$ through Point-MixUp [9]. This step generates the
adversarial mixed data
$D_{AM}=\\{\tilde{p}^{i}_{AM},\hat{l}^{i}_{AM}\\}^{N_{S}}_{i=1}$. In parallel,
we also mix the unlabeled target data $D_{TU}$ with the labeled target data
$D_{TL}$ without Adversarial Point Augmentation. This generates the point-
mixed data $D_{PM}=\\{\tilde{p}^{i}_{PM},\hat{l}^{i}_{PM}\\}^{N_{S}}_{i=1}$.
Both mixed data $D_{AM}$ and $D_{PM}$ are used to train the student model, as
described in the next section. Note that this mixup operation is conducted
with the probability $P_{am}$. Without the mixup operation, we let
$D_{AM}=D_{TU}$ and $D_{PM}=D_{adv}$.

###  III-D Training

Our TODA is trained through two-stage training process. In the first stage,
the teacher model $f_{t}$ is trained with the data $D_{TM}$ generated by
TargetMix through the detection loss,

$\displaystyle\mathcal{L}_{det}^{tm}=\mathcal{L}^{tm}_{cls}+\mathcal{L}^{tm}_{reg},$
(6)

where $\mathcal{L}_{reg}^{tm}$ and $\mathcal{L}_{cls}^{tm}$ are the standard
smoothed-L1 loss and focal loss [35], respectively. In the next stage, the
student model was trained while freezing the teacher model. The loss function
used to train the student model is given by

$\displaystyle\mathcal{L}_{adv}=\mathcal{L}^{am}_{det}+\mathcal{L}^{pm}_{det}+\lambda\mathcal{L}_{cons},$
(7)

where $\lambda$ is the regularization parameter, and $\mathcal{L}_{det}^{am}$
and $\mathcal{L}_{det}^{pm}$ are the detection loss terms associated with the
adversarial mixed data $D_{AM}$ and the point mixed data $D_{PM}$,
respectively.

We also consider the Consistency Loss $\mathcal{L}_{cons}$ to enforce the
consistency between the detection results obtained from $D_{PM}$ and those
obtained from $D_{AM}$. Specifically, $\mathcal{L}_{cons}$ is expressed as

$\displaystyle\mathcal{L}^{i}_{cons}=\frac{\Sigma^{N^{i}_{AMB}}_{k=1}||b^{i,k}_{am}-\tilde{b}^{i,k}_{pm}||_{2}+\Sigma^{N^{i}_{PMB}}_{k=1}||b^{i,k}_{pm}-\tilde{b}^{i.k}_{am}||_{2}}{N^{i}_{AMB}+N^{i}_{PMB}},$
(8)
$\displaystyle\mathcal{L}_{cons}=\frac{\Sigma^{N_{TU}}_{i=1}\mathcal{L}^{i}_{cons}}{N_{TU}},$
(9)

where $b^{i,k}_{am}$ and $b^{i,k}_{pm}$ denote the $k$th predicted bounding
boxes obtained from the $i$th sample, respectively. To quantify the distance
of $b^{i,k}_{am}$ from $b^{i,k}_{pm}$, we find $\tilde{b}^{i,k}_{am}$ among
the set $\\{b^{i,k}_{am}\\}_{k=1}^{N_{AMB}^{i}}$ which is closest to
$b^{i,k}_{pm}$. Similarly, we also find $\tilde{b}^{i,k}_{pm}$ among the set
$\\{b^{i,k}_{pm}\\}_{k=1}^{N_{PMB}^{i}}$ which is closest to $b^{i,k}_{am}$.
Each bounding box is represented by center coordinates $(x,y,z)$ and size
offset $(w,l,h)$ and $||.||_{2}$ denotes the $\ell_{2}$ norm, and
$N_{AMB}^{i}$ and $N_{PMB}^{i}$ represent the number of detection boxes for
the $i$th sample in $D_{AM}$ and that in $D_{PM}$, respectively. The overall
training process is presented in Algorithm 1.

(a)

(b)

Figure 4: t-SNE visualization of features: (a) unlabeled data (green) versus
labeled data (red), (b) adversarial examples (green) versus labeled data (red)
within the target domain. These features are extracted from the final layer of
the teacher model trained with TargetMix. TABLE I:  Comparison of domain
adaptation performance on Waymo to nuScenes with different amounts of target
labels for the car class. We report AP, NDS, and the closed gap based on
nuScenes metric. The best adaptation result is indicated in bold.

Method 0.5% 1% 5% 10% AP / NDS Closed Gap AP / NDS Closed Gap AP / NDS Closed
Gap AP / NDS Closed Gap (AP / NDS) (AP / NDS) (AP / NDS) (AP / NDS) Source
Only 42.6 / 50.3 +0% / +0% 42.6 / 50.3 +0% / +0% 42.6 / 50.3 +0% / +0% 42.6 /
50.3 +0% / +0% ST3D [8] 43.7 / 50.2 +3.1% / -0.5% 43.7 / 50.2 +3.1% / -0.5%
43.7 / 50.2 +3.1% / -0.5% 43.7 / 50.2 +3.1% / -0.5% Labeled Target 36.0 / 37.7
-18.4% / -64.2% 37.2 / 38.1 -15.1% / -62.2% 61.0 / 53.2 +51.4% / +14.8% 65.6 /
58.2 +64.2% / +40.3% Co-training 47.5 / 52.7 +13.7% / +12.2% 51.4 / 54.6
+24.6% / +21.9% 57.7 / 58.0 +42.2% / +39.3% 59.4 / 58.9 +46.9% / +43.9% SSDA3D
[9] 70.3 / 65.1 +77.4% / +75.5% 73.4 / 67.1 +86.0% / +85.7% 76.2 / 68.8 +93.9%
/ +94.4% 78.8 / 70.9 +101.1% / +105.1% Ours 73.7 / 67.3 +86.9% / +86.7% 75.6 /
68.5 +92.2% / +92.9% 79.0 / 71.1 +101.7% / +106.1% 79.3 / 71.4 +102.5% /
+107.7% Oracle 78.4 / 69.9 +100% / +100% 78.4 / 69.9 +100% / +100% 78.4 / 69.9
+100% / +100% 78.4 / 69.9 +100% / +100%

##  IV Experiments

###  IV-A Datasets

We consider the setup where domain adaptation is conducted for two scenarios:

  * •

Transfer from Waymo dataset [14] to nuScenes dataset [16]

  * •

Transfer from nuScenes dataset to KITTI dataset [15].

The Waymo dataset provides 160k labeled training samples collected with
64-beam LiDAR with a 20-degree VFOV of 20 degrees (from $-17.6^{\circ}$ to
$2.4^{\circ}$) while nuScenes provides 28k frames of labeled training samples
collected with 32-beam LiDAR and has a VFOV of 40 degrees (from
$-30.0^{\circ}$ to $10.0^{\circ}$). KITTI provides 7,481 frames of labeled
training samples collected with 64-beam LiDAR and has a VFOV of 30 degrees
(from $-23.6^{\circ}$ to $3.2^{\circ}$). We use 0.5%, 1%, 5%, and 10% labels
for nuScenes dataset and 1% labels for KITTI dataset, and the remaining data
were utilized as unlabeled data.

We evaluated the performance of our method using both the nuScenes and KITTI
3D object detection metrics. For the nuScenes metric, we utilized the official
nuScenes Detection Score (NDS) [16] and Average Precision (AP) for the car
category. AP represents the average precision values obtained across different
thresholds of $d=0.5,1,2,$ and $4$ meters calculated based on the BEV center
distance. NDS provides a comprehensive metric incorporating AP as well as
errors in attributes, classification, localization, and velocity. For a fair
comparison with existing methods, we also adopted the official KITTI 3D object
detection metric. The KITTI metric calculates the AP for both BEV IoU and 3D
IoU under an IoU threshold of 0.7 over 40 recall positions, specifically for
the car category. Following the approach of ST3D [8], we measured the
reduction in AP and NDS relative to the Oracle performance, i.e.,
$\frac{AP_{model}-AP_{source\ only}}{AP_{oracle}-AP_{source\ only}}$ and
$\frac{NDS_{model}-NDS_{source\ only}}{NDS_{oracle}-NDS_{source\ only}}$. We
refer to this metric as the closed gap. The Oracle performance was obtained by
conducting supervised learning using labels from the entire target-domain
dataset.

TABLE II: Comparison of domain adaptation performance on Waymo to nuScenes using SECOND-IoU based on KITTI metric. SSDA3D and TODA utilized an additional 1% of target-domain labeled data. Method |  $AP_{BEV}$ / $AP_{3D}$ | Closed Gap
---|---|---
Source only | 32.9 / 17.2 | +0% / +0%
SN [11] | 33.2 / 18.6 | +1.7% / +7.5%
ST3D [8] | 35.9 / 20.2 | +15.9% / +16.7%
ST3D++ [13] | 35.7 / 20.9 | +14.7% / +20.9%
L.D [36] | 40.7 / 22.9 | +41.1% / +32.2%
DTS [37] | 41.2 / 23.0 | +43.7% / +32.8%
SSDA3D [9] | 46.6 / 29.6 | +72.1% / +70.1%
TODA (Ours) | 48.1 / 30.2 | +80.0% / +73.4%
Oracle | 51.9 / 34.9 | +100% / +100%

###  IV-B Implementation Details

We implemented the CenterPoint [4] and SECOND-IoU [3] using the OpenPCDet [38]
codebase. We followed the respective training schedule used in CenterPoint [4]
and SECOND-IoU. For the Waymo to nuScenes adaptation, the detection ranges for
$x$, $y$, and $z$ axes were set to $[-54.0,54.0]$, $[-54.0,54.0]$, and
$[-5.0,4.8]$ meters, and the voxel size was set to $[0.075,0.075,0.2]$. In the
case of nuScenes to KITTI adaptation, the detection ranges in $x$, $y$, and
$z$ axes were set to $[-76.2,76.2]$, $[-76.2,76.2]$, and $[-3.0,5.0]$ meters
with the same voxel size of $[0.075,0.075,0.2]$. The intensity of the point
cloud was normalized to have a value between $[0,1]$. We applied data
augmentation techniques, including random flip along X and Y axes, random
rotation, random scaling, and GT sampling. GT sampling was only applied to the
labeled data in the target domain for the second stage. The probability
$P_{tm}$ used in TargetMix was set to 0.4. The probability $P_{am}$ in AdvMix
was set to 0.6. The hyperparameters $\lambda$, $\rho$, and $\epsilon$ were
empirically chosen to 1, 0.5, and 0.001, respectively. All experiments were
conducted on four 24GB RTX 3090TI GPUs.

TABLE III: Comparison of domain adaptation performance on nuScenes to KITTI using SECOND-IoU based on KITTI metric. SSDA3D and TODA utilized an additional 1% of target-domain labeled data. Method |  $AP_{BEV}$ / $AP_{3D}$ | Closed Gap
---|---|---
Source only | 51.8 / 17.9 | +0% / +0%
SN [11] | 59.7 / 37.6 | +25.1% / +35.4%
ST3D [8] | 75.9 / 54.1 | +76.6% / +59.5%
ST3D++ [13] | 80.5 / 62.4 | +91.1% / +80.0%
DTS [37] | 81.4 / 66.6 | +94.0% / +87.6%
SSDA3D [9] | 81.5 / 67.4 | +94.3% / +89.0%
TODA (Ours) | 82.7 / 68.6 | +98.1% / +91.2%
Oracle | 83.3 / 73.5 | +100% / +100%
TABLE IV: Ablation study for evaluating the contribution of each component of
TODA on the nuScenes validation dataset.

Method TargetMix AdvMix AP / NDS Source Only 37.2 / 38.1 Co-training 51.4 /
54.6 TODA $\checkmark$ 71.2 / 65.9 $\checkmark$ $\checkmark$ 75.6 / 68.5

###  IV-C Main Results

We evaluated the performance of our SSDA model using CenterPoint. In the
nuScenes dataset, we utilized labels for 0.5%, 1%, 5%, and 10% of the target
domain data, corresponding to 141, 282, 1407, and 2813 frames, respectively.
Table I presents the performance of TODA in the Waymo to nuScenes adaptation
task, using the nuScenes metric. We compare TODA with the existing domain
adaptation methods including Co-training, Labeled Target, ST3D [8], and SSDA3D
[9]. Co-training utilized supervised learning using the labeled data in both
source and target domains while Labeled Target employed supervised learning
solely utilizing the labeled data in the target domain. Table I shows that
TODA consistently outperforms all other methods by significant margins. When
0.5% of labeled data are used in the target domain, TODA demonstrates notable
performance gains of 3.4% in AP and 2.2% in NDS over SSDA3D, the current
state-of-the-art method. TODA also achieves more than 85% closed gap from the
Oracle performance. Even with 1% labeled target-domain data, TODA achieves
improvements of 2.2% in AP and 1.4% in NDS over SSDA3D. It also achieves above
90% closed gap in both metrics. Remarkably, when 5% and 10% labeled data are
utilized, TODA even surpasses the Oracle performance. This would be possibly
because TODA leverages both source-domain and target-domain data, while the
Oracle performance is obtained using the target-domain dataset only. In
summary, TODA achieves performance comparable to the Oracle performance while
significantly reducing annotation costs.

TABLE V: Ablation study for the TargetMix module. Polar, Enhance., and Match.,
indicate Polar Coordinate-based Mix, Enhanced Mix Strategy, and LiDAR
Distribution Matching, respectively.

Method Polar Enhance. Match. AP / NDS CutMix [9] 66.8 / 63.4 TargetMix
$\checkmark$ 68.3 / 63.8 $\checkmark$ $\checkmark$ 69.6 / 64.5 $\checkmark$
$\checkmark$ $\checkmark$ 71.2 / 65.9

TABLE VI: Ablation study for the AdvMix module. Adv., Cons., MixUp., indicate
Adversarial Point Augmentation, Consistency Loss, and Point-MixUp,
respectively.

Method Adv. Cons. MixUp AP / NDS TargetMix 71.2 / 65.9 AdvMix $\checkmark$
73.2 / 67.1 $\checkmark$ $\checkmark$ 73.5 / 67.2 $\checkmark$ $\checkmark$
$\checkmark$ 75.6 / 68.5

TABLE VII: Ablation study for the probability of mixing augmentation $P_{tm}$
and $P_{am}$.

$P_{tm}$ | AP
---|---
0.1 | 70.1
0.2 | 71.2
0.3 | 70.1
0.4 | 69.8
(a)

$P_{am}$ | AP
---|---
0.4 | 78.1
0.5 | 78.5
0.6 | 79.0
0.7 | 78.8
(b)

Figure 5: Comparison with SSDA3D in each stage for different percentages:
Performance comparison between TODA and SSDA3D across various sizes of labeled
data (0.1%, 0.2%, 0.5%, 1%, 5%, and 10%) TABLE VIII: The performance of TODA
with 0.1% and 0.2% of target-domain labeled data.

Method 0.1% 0.2% Labeled Target fail fail Co-training 50.3 52.2 SSDA3D [9]
62.0 67.6 TODA 69.7 71.8 Oracle 78.4 78.4

We further validate the effectiveness of our method in comparison with various
latest UDA techniques including SN [11], ST3D [8], ST3D++ [13], L.D [36], and
DTS [37]. For fair comparison, we implemented TODA on the SECOND-IoU model and
conducted evaluation using the KITTI metrics for the Waymo to nuScenes
adaptation task. Table II shows that the proposed TODA method still maintains
performance gains over other UDA methods. While UDA methods exhibit limitation
in reducing the domain gap, TODA achieves a significantly higher closed gap of
+80.0% using only 1% of the labeled target data.

Table III shows the performance of TODA on the nuScenes to KITTI adaptation
scenario as well. Although the UDA methods achieve performance close to the
oracle, TODA generalizes well to the nuScenes to KITTI setup. Notably, TODA
achieves a closed gap of 98.1/91.2% relative to the Oracle. These results
highlight the effectiveness of our SSDA approach in adapting to different
target domains, even with 1% of labeled data.

###  IV-D Ablation Studies

####  IV-D1 Contribution of Each Component

We conducted ablation studies to assess the contribution of each component of
TODA to the overall performance. The evaluation was conducted using the
nuScenes validation set on the Waymo to nuScenes task. Table IV presents the
results when 1% of the target-domain labeled data are used. We sequentially
enabled TargetMix and AdvMix on top of the baseline method based on Co-
training. The inclusion of TargetMix results in a notable performance gain of
19.8% in AP and 11.3% in NDS, highlighting its significant contribution.
Incorporating AdvMix leads to a performance improvement of 4.4% in AP and 2.6%
in NDS.

(a)  (b)

Figure 6: Comparison of detection results: (a) SSDA3D and (b) TODA. All
samples are from nuScenes val split. The red box represents the ground truth,
and the blue box indicates the predicted bounding box.

####  IV-D2 Sub-components of TargetMix

Table V illustrates the ablation study evaluating the contribution of three
components within the TargetMix module: Polar Coordinate-based Mix, Enhanced
Mix Strategy, and LiDAR Distribution Matching. The Enhanced Mix Strategy
refers to the idea of segmenting the area into $K$ segments and allocating the
LiDAR points accordingly. LiDAR Distribution Matching refers to the method
that adjusts LiDAR data to match the configuration of the target-domain LiDAR.
While the naive Polar Coordinate-based Mix demonstrates only a slight
improvement over the CutMix baseline, its impact becomes significant when
combined with our Enhanced Mix Strategy. This Enhanced Mix Strategy achieves a
performance gain of 2.8% in AP and 2.1% in NDA. Additionally, the LiDAR
Distribution Matching method further enhances performance by 1.6% in AP and
1.4% in NDS.

####  IV-D3 Sub-components of AdvMix

Table VI presents the ablation study assessing the impact of each component
within the AdvMix module. We utilized a model trained with TargetMix as our
baseline. Three components were considered: Adversarial Point Augmentation,
Consistency Loss, and Point-Mixup. Adversarial Point Augmentation results in a
performance gain of 2.0% in AP and 1.2% in NDS. Additionally, Consistency Loss
contributes to an increase in AP by 0.3% and NDS by 0.1%. Finally, Point-MixUp
leads to further improvements of 2.1% in AP and 1.3% in NDS.

####  IV-D4 Probabilities $P_{tm}$ and $P_{am}$ of TargetMix and AdvMix

Table VIIb presents the performance of TODA as a function of the parameters
$P_{tm}$ and $P_{am}$. Table VIIb (a) shows the performance of the first stage
with varying $P_{tm}$ values, while Table VIIb (b) illustrates the final TODA
performance with varying $P_{am}$ values using a model trained with
$P_{tm}=0.2$. The results demonstrate that TODA exhibits robust performance
with respect to both $P_{tm}$ and $P_{am}$, with only slight degradation under
different settings. The optimal performance is achieved with $P_{tm}=0.2$ and
$P_{am}=0.6$.

####  IV-D5 Scenarios Using Extremely Low Number of Labels

In Table VIII, we evaluate the performance of TODA in scenarios where the
percentage of labeled target-domain data is extremely low, e.g., 0.1% (28
frames) and 0.2% (56 frames). Training exclusively with such small number of
labeled targets failed due to insufficient target-domain data. In contrast,
TODA achieves AP values of 69.7% and 71.8% respectively, surpassing SSDA3D by
6.3% and 4.2% respectively. Remarkably, TODA achieves 75% and 81% of closed
gap even with 0.1% and 0.2% labeled data respectively. These results
demonstrate the potential of TODA in highly data-constrained environments.

Figure 5 presents a performance comparison between TODA and SSDA3D across
various sizes of labeled data (0.1%, 0.2%, 0.5%, 1%, 5%, and 10%). Performance
was assessed at both the initial and secondary stages of each detector. We
noted an increasing performance gap between TODA and SSDA3D at both stages as
the percentage of labeled data decreased. This trend seems to be attributed to
proposed LiDAR Distribution Matching that conducts the adaptation of LiDAR
data distribution without using labeled data. This leads to notable
performance improvements for TODA in the initial stage. Additionally,
Adversarial Point Augmentation reshapes the distribution of unlabeled data in
the target domain, enabling more effective feature alignment between labeled
and unlabeled data. This boosts the performance of our pseudo-label based
semi-supervised learning in the second stage.

###  IV-E Qualitative Results

We present some qualitative results. We considered the setup where 1% of the
labeled data are used in the target domain. The experiments were conducted on
the nuScenes validation set.

In Fig. 4, we present visualizations of feature distributions utilizing t-SNE
[39] under both pre- and post-application of Adversarial Point Augmentation.
Fig. 4 (a) highlights the distribution gap between the labeled and unlabeled
data, while Fig. 4 (b) shows the gap between the labeled data and the
adversarially perturbed unlabeled data. Notably, Adversarial Point
Augmentation demonstrates its effectiveness in mitigating the distribution
shift.

Fig. 6 illustrates the detection results produced by both the existing method
and the proposed TODA method. The figures in the left column depict the
detection results attained by SSDA3D, while those in the right column show the
results of the proposed TODA method. We observe that TODA demonstrates
enhanced detection results, effectively identifying objects that were
previously either missed or inaccurately detected by SSDA3D.

##  V Conclusions

In this paper, we introduced TODA, an SSDA framework for 3D object detection
based on a target-oriented domain augmentation strategy. To mitigate the
disparity in data distribution between the source and target domains, we
introduced TargetMix. TargetMix utilizes an inter-domain mixup augmentation
strategy within a polar coordinate system, considering the LiDAR scanning
mechanism. Additionally, TargetMix incorporates LiDAR Distribution Matching to
adapt the source domain data to align with the configurations of the target-
domain LiDAR sensor. Additionally, we proposed AdvMix, which adds adversarial
perturbation to the unlabeled data to mitigate intra-domain disparity. We
optimized the perturbation direction to maximize detection performance,
enabling AdvMix to generate consistent representations of both labeled and
unlabeled data in the target domain. By integrating TargetMix and AdvMix, TODA
effectively utilizes both labeled and unlabeled data for domain adaptation.
Our evaluation demonstrated that TODA achieved significant performance gains
over existing domain adaptation methods and approached performance levels
close to the Oracle performance.

Moving forward, we aim to enhance TODA’s capabilities to cope with adverse
weather conditions and low-resolution LiDAR environments, as well as explore
its potential for other sparse data modalities like radar. These advancements
will expand TODA’s usefulness and offer promising avenues for future research
in domain adaptation and 3D object detection.

## Acknowledgments

This work was partly supported by Institute of Information & communications
Technology Planning & Evaluation (IITP) grant funded by the Korea government
(MSIT) [No.2021-0-01343-004, Artificial Intelligence Graduate School Program
(Seoul National University)], the National Research Foundation of Korea (NRF)
grant funded by the Korea government (MSIT) [No.2020R1A2C2012146], and
Information & communications Technology Promotion(IITP) grant funded by the
Korea government(MSIP) [No.2021-0-01314, Development of driving environment
data stitching technology to provide data on shaded areas for autonomous
vehicles].

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## Biography Section

|  Yecheol Kim received a B.S. degree in Electrical Engineering from the
Hanyang University, Seoul, South Korea, in 2018. He is currently pursuing a
Ph.D. degree in the Hanyang University. His research interests include multi-
modal object detection, domain adaptation, and efficient architecture for
autonomous driving.
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|  Junho Lee received a B.S. degree in Electrical Engineering from the Hanyang
University, Seoul, South Korea, in 2018. He is currently pursuing a Ph.D.
degree in the Hanyang University. His research interests reinforcement
learning, domain adaptation, and domain generalization for autonomous driving.
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|  Changsoo Park received B.S, M.S and Ph.D. degrees from the Electrical
Engineering, KAIST. In 2015, he joined Samsung Electronics, where he
participated in research on autonomous driving and robot. Since 2019, he has
been researching autonomous driving at Kakao mobility.
---|---
|  Hyung Won Kim received the B.S., M.S. and Ph.D. degrees in Electronic
Engineering from Hanyang University. From 2015 to 2021, he was with the
Hyundai Mobis where his major research topics included target tracking and
sensor fusion. He has been working at Kakao Mobility since 2021. His research
area includes sensor fusion and machine learning.
---|---
|  Inho Lim received B.S degres from the Information and Technology
Department, Ajou University. In 2012, he joined Samsung Electronics, Suwoun,
Korea, where he participated in the software engineering research team. Since
2017, he has participated in autonomous driving SW research at Samsung
Advanced Institute of Technology. Since 2019, he has been researching the
Localization and Perception technologies for autonomous driving in Kakao
Mobility Corp.
---|---
|  Christopher Chang Christopher Chang received a B.S. degree from Department
of Electrical Engineering, Seoul National University, and M.S. and Ph.D.
degrees in Electrical Engineering from California Institute of Technology,
Pasadena CA. Since 2012, he had been working with Samsung Electronics and
Hyundai Motor Company, where he developed corporate strategies and business
roadmaps. In 2020, he joined Kakao Mobility Corp. where he has been leading
strategy, business development, and R&D for the next generation mobilities
including autonomous driving, robotics, digital twin, and urban air mobility.
---|---
|  Jun Won Choi earned his B.S. and M.S. degrees from Seoul National
University and his Ph.D. from the University of Illinois at Urbana-Champaign.
Following his studies, he joined Qualcomm in San Diego, USA, in 2010. From
2013 to 2024, he served as a faculty member in the Department of Electrical
Engineering at Hanyang University. Since 2024, he has held a faculty position
in the Department of Electrical and Computer Engineering at Seoul National
University. He currently serves as an Associate Editor for both IEEE
Transactions on Intelligent Transportation Systems, IEEE Transactions on
Vehicular Technology, International Journal of Automotive Technology. His
research spans diverse areas including signal processing, machine learning,
robot perception, autonomous driving, and intelligent vehicles.
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