The main challenge is that the data is noisy and highly variable . To this end , we present a tree-aware metric-learning approach and demonstrate that such learned embeddings offer excellent transfer to semantic segmentation tasks , especially when training data is limited . Our approach reduces the relative error by 10.2 % with 8 training examples , by 11.72 % with 120 training examples on the ShapeNet semantic segmentation benchmark , in comparison to the network trained from scratch . Afterwards , we review the developed techniques according to a taxonomy based on the scene understanding tasks . This covers holistic indoor scene understanding as well as subtasks such as scene classification , object detection , pose estimation , semantic segmentation , 3D reconstruction , saliency detection , physics-based reasoning and affordance prediction . Later on , we summarize the performance metrics used for evaluation in different tasks and a quantitative comparison among the recent state-of-the-art techniques . Stand detection has been barely addressed in the literature which has mainly focused , in forested environments , on individual tree extraction and tree species classification . From a methodological point of view , stand detection can be considered as a semantic segmentation problem . It offers two advantages . section : Abstract Pixel - level labelling tasks , such as semantic segmentation , play a central role in image understanding . Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixellevel labelling tasks . Our framework , while conceptually simple and theoretically abundant , is also practically effective and computationally efficient . We demonstrate substantial improvements over state - of - the - art instance segmentation for object proposal generation , as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation . section : Introduction document : Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks ( CNNs ) . We show how to improve semantic segmentation through the use of contextual information ; specifically , we explore ‘ patch - patch ’ context between image regions , and ‘ patch - background ’ context . Jain and Seung proposed to use Convolutional Neural Networks ( CNN ) to denoise natural images . Their framework is the same as the recent Fully Convolutional Neural Networks ( FCN ) for semantic image segmentation and other tasks such as super - resolution , although their network is not as deep as today ’s models . Their network accepts an image as the input and produces an entire image as the output through four hidden layers of convolutional filters . document : Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Deep Convolutional Neural Networks ( DCNNs ) have recently shown state of the art performance in high level vision tasks , such as image classification and object detection . This study investigates unsupervised phoneme segmentation without using prior information on linguistic contents and acoustic models of an input sequence . The authors formulate the unsupervised segmentation as an optimal problem by means of maximum likelihood , and show that the optimal segmentation corresponds to minimising the coding length of the input sequence . Under different assumptions , five different objective functions are developed , namely log determinant , rate distortion ( RD ) , Bayesian log determinant , Mahalanobis distance and Euclidean distance objectives . We present a new unsupervised segmentation based active contours model and local region texture descriptor . The proposed local region texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework . Unsupervised image segmentation is of essential relevance for many computer vision applications and remains a difficult task despite of decades of intensive research . In particular , the parameter selection problem has not received the due attention in the past . Then , a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs . Three clustering methods , i.e. , K-mean , soft clustering and multiscale clustering , are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples . The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithms can achieve high classification accuracy . Considering the lack of labeled training data sets for the supervised change detection task , in this letter , we try to relieve this problem by proposing a convolutional neural network (CNN)-based change detection method with a newly designed loss function to achieve transfer learning among different data sets . To reach this goal , we first pretrain a U-Net model on an open source data set by taking advantages of the relatively sufficient training data used for the supervised semantic segmentation task . Then , we minimize a skillfully designed loss function to combine the high-level features extracted from the pretrained model and the semantic information contained in the change detection data set , by which a transfer learning is achieved . We consider an unsupervised image segmentation problem --- from figure-ground separation to multiregion partitioning --- that consists of maximal distribution separation ( in terms of mutual information ) with spatial regularity ( total variation regularization ) , which is what we call information-theoretic minimal partitioning . Adopting the bounded variation framework , we provide an in-depth analysis of the problem which establishes theoretical foundations and investigates the structure of the associated energy from a variational perspective . The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing . The high dimensionality of hyperspectral data , presence of substantial noise , and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images . This method relies on the benefit of unsupervised learning , in the form of image reconstruction , for network training . To this end , it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a single generative model . This embedding generates an output image by superimposing an input image on its segmentation map . AbstractUnsupervised semantic segmentation in the time series domain is a much studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data . However , the current techniques have several shortcomings , which have limited the adoption of time series semantic segmentation beyond academic settings for four primary reasons . We also provide qualitative connections to traditional methods for component analysis ( e.g. Grassmann averages , PCA , and NMF ) . The effectiveness of our approach is validated through synthetic data and on the MSRC2 and Sift Flow datasets , demonstrating competitive results in unsupervised and weakly-supervised semantic segmentation . It follows that during inference , we can threshold with a bandwidth around a cluster center to select all embeddings belonging to that cluster . Thresholding in this case means selecting all embeddings that lie within a hypersphere with radius around the cluster center : For the tasks of classification and semantic segmentation , with a fixed set of classes , this leads to a simple strategy for post - processing the output of the network into discrete classes : after training , calculate the cluster centers of each class over the entire training set . During inference , threshold around each of the cluster centers to select all pixels belonging to the corresponding semantic class . They show with examples that mIoU favours region smoothness and does not evaluate boundary accuracy , a point also alluded to recently by the authors of FCN . Hence they propose to complement the mIoU metric with a boundary measure based on the Berkeley contour matching score commonly used to evaluate unsupervised image segmentation quality . Csurka et al . simply extend this to semantic segmentation and show that the measure of semantic contour accuracy used in conjunction with the mIoU metric agrees more with human ranking of segmentation outputs . With the availability of cheap RGB-D sensors the field of indoor semantic segmentation has seen a lot of progress . Still it remains unclear how to deal with 3D semantic segmentation in the best way . We propose a novel 2D-3D label transfer based on Bayesian updates and dense pairwise 3D Conditional Random Fields . To balance the performance and complexity of the model , this paper simplifies the PointConv which called Mini-PointConv to reduce the occupation of network computing resources while ensuring the accuracy of the model segmentation results . Furthermore , the method of ScanNet is adopted to test the Mini-PointConv , which shows that the improved network has achieved a good experimental result in 3D scene semantic segmentation tasks and gained a better performance as balance as well . Finally , the Mini-PointConv is tested in a variety of indoor environments using laser radar and obtain a good indoor 3D point cloud recognition result . Extensive experiments on challenging benchmarks show that our proposed model properly captures the local patterns to provide discriminative point set features . For the task of 3D scene semantic segmentation , our method outperforms the state-of-the-art on the challenging Stanford Large-Scale 3D Indoor Spaces Dataset(S3DIS ) by a large margin . These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection . The extensive experiments on a pelvic CT image dataset , a multi-modal brain tumor dataset , and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation , respectively . Our experimental results also show that besides increasing the network complexity , raising the resolution of semantic feature maps can largely affect the overall model performance . We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains . In this work we propose Shooting Labels , the first 3D labeling tool for dense 3D semantic segmentation which exploits Virtual Reality to render the labeling task as easy and fun as playing a video-game . Our tool allows for semantically labeling large scale environments very expeditiously , whatever the nature of the 3D data at hand ( e.g. pointclouds , mesh ) . Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data , and how to smartly fuse information from frame to frame . We propose a novel fusion-aware 3D point convolution which operates directly on the geometric surface being reconstructed and exploits effectively the inter-frame correlation for high-quality 3D feature learning . We also introduce local region based energies , overcoming the limitations of the original method . The feasibility of real-time 3D segmentation in challenging images using the proposed algorithm is demonstrated in liver tumor data . The segmentation accuracy is comparable to a recently proposed state-of-the-art algorithm , but with CPU times below 0.1s . In the context of cancer delineation using positron emission tomography datasets , we present an innovative approach which purpose is to tackle the real-time , three-dimensional segmentation task in a full , or at least nearly full automatized way . The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset . We present a novel method for the semantic segmentation of 3D data in large-scale urban environment . Our method significantly reduces the intensive labeling cost in previous works by automatically generating training data from the input data . Due to the high complexity of spinal structures , the segmentation of 3D multiple spinal structures with consistently reliable and high accuracy is still a significant challenge . We developed and validated a relatively complete solution for the simultaneous 3D semantic segmentation of multiple spinal structures at the voxel level named as the S3egANet . Firstly , S3egANet explicitly solved the high variety and variability of complex 3D spinal structures through a multi-modality autoencoder module that was capable of extracting fine-grained structural information . section : Acknowledgment : This work was funded by the Christian Doppler Laboratory for Semantic 3D Computer Vision . section :
The main challenge is that the data is noisy and highly variable . To this end , we present a tree-aware metric-learning approach and demonstrate that such learned embeddings offer excellent transfer to semantic segmentation tasks , especially when training data is limited . Our approach reduces the relative error by 10.2 % with 8 training examples , by 11.72 % with 120 training examples on the ShapeNet semantic segmentation benchmark , in comparison to the network trained from scratch .
Afterwards , we review the developed techniques according to a taxonomy based on the scene understanding tasks . This covers holistic indoor scene understanding as well as subtasks such as scene classification , object detection , pose estimation , semantic segmentation , 3D reconstruction , saliency detection , physics-based reasoning and affordance prediction . Later on , we summarize the performance metrics used for evaluation in different tasks and a quantitative comparison among the recent state-of-the-art techniques .
Stand detection has been barely addressed in the literature which has mainly focused , in forested environments , on individual tree extraction and tree species classification . From a methodological point of view , stand detection can be considered as a semantic segmentation problem . It offers two advantages .
section : Abstract Pixel - level labelling tasks , such as semantic segmentation , play a central role in image understanding . Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixellevel labelling tasks .
Our framework , while conceptually simple and theoretically abundant , is also practically effective and computationally efficient . We demonstrate substantial improvements over state - of - the - art instance segmentation for object proposal generation , as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation . section : Introduction
document : Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks ( CNNs ) . We show how to improve semantic segmentation through the use of contextual information ; specifically , we explore ‘ patch - patch ’ context between image regions , and ‘ patch - background ’ context .
Jain and Seung proposed to use Convolutional Neural Networks ( CNN ) to denoise natural images . Their framework is the same as the recent Fully Convolutional Neural Networks ( FCN ) for semantic image segmentation and other tasks such as super - resolution , although their network is not as deep as today ’s models . Their network accepts an image as the input and produces an entire image as the output through four hidden layers of convolutional filters .
document : Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Deep Convolutional Neural Networks ( DCNNs ) have recently shown state of the art performance in high level vision tasks , such as image classification and object detection .
This study investigates unsupervised phoneme segmentation without using prior information on linguistic contents and acoustic models of an input sequence . The authors formulate the unsupervised segmentation as an optimal problem by means of maximum likelihood , and show that the optimal segmentation corresponds to minimising the coding length of the input sequence . Under different assumptions , five different objective functions are developed , namely log determinant , rate distortion ( RD ) , Bayesian log determinant , Mahalanobis distance and Euclidean distance objectives .
We present a new unsupervised segmentation based active contours model and local region texture descriptor . The proposed local region texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework .
Unsupervised image segmentation is of essential relevance for many computer vision applications and remains a difficult task despite of decades of intensive research . In particular , the parameter selection problem has not received the due attention in the past .
Then , a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs . Three clustering methods , i.e. , K-mean , soft clustering and multiscale clustering , are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples . The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithms can achieve high classification accuracy .
Considering the lack of labeled training data sets for the supervised change detection task , in this letter , we try to relieve this problem by proposing a convolutional neural network (CNN)-based change detection method with a newly designed loss function to achieve transfer learning among different data sets . To reach this goal , we first pretrain a U-Net model on an open source data set by taking advantages of the relatively sufficient training data used for the supervised semantic segmentation task . Then , we minimize a skillfully designed loss function to combine the high-level features extracted from the pretrained model and the semantic information contained in the change detection data set , by which a transfer learning is achieved .
We consider an unsupervised image segmentation problem --- from figure-ground separation to multiregion partitioning --- that consists of maximal distribution separation ( in terms of mutual information ) with spatial regularity ( total variation regularization ) , which is what we call information-theoretic minimal partitioning . Adopting the bounded variation framework , we provide an in-depth analysis of the problem which establishes theoretical foundations and investigates the structure of the associated energy from a variational perspective .
The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing . The high dimensionality of hyperspectral data , presence of substantial noise , and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images .
This method relies on the benefit of unsupervised learning , in the form of image reconstruction , for network training . To this end , it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a single generative model . This embedding generates an output image by superimposing an input image on its segmentation map .
AbstractUnsupervised semantic segmentation in the time series domain is a much studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data . However , the current techniques have several shortcomings , which have limited the adoption of time series semantic segmentation beyond academic settings for four primary reasons .
We also provide qualitative connections to traditional methods for component analysis ( e.g. Grassmann averages , PCA , and NMF ) . The effectiveness of our approach is validated through synthetic data and on the MSRC2 and Sift Flow datasets , demonstrating competitive results in unsupervised and weakly-supervised semantic segmentation .
It follows that during inference , we can threshold with a bandwidth around a cluster center to select all embeddings belonging to that cluster . Thresholding in this case means selecting all embeddings that lie within a hypersphere with radius around the cluster center : For the tasks of classification and semantic segmentation , with a fixed set of classes , this leads to a simple strategy for post - processing the output of the network into discrete classes : after training , calculate the cluster centers of each class over the entire training set . During inference , threshold around each of the cluster centers to select all pixels belonging to the corresponding semantic class .
They show with examples that mIoU favours region smoothness and does not evaluate boundary accuracy , a point also alluded to recently by the authors of FCN . Hence they propose to complement the mIoU metric with a boundary measure based on the Berkeley contour matching score commonly used to evaluate unsupervised image segmentation quality . Csurka et al . simply extend this to semantic segmentation and show that the measure of semantic contour accuracy used in conjunction with the mIoU metric agrees more with human ranking of segmentation outputs .
With the availability of cheap RGB-D sensors the field of indoor semantic segmentation has seen a lot of progress . Still it remains unclear how to deal with 3D semantic segmentation in the best way . We propose a novel 2D-3D label transfer based on Bayesian updates and dense pairwise 3D Conditional Random Fields .
To balance the performance and complexity of the model , this paper simplifies the PointConv which called Mini-PointConv to reduce the occupation of network computing resources while ensuring the accuracy of the model segmentation results . Furthermore , the method of ScanNet is adopted to test the Mini-PointConv , which shows that the improved network has achieved a good experimental result in 3D scene semantic segmentation tasks and gained a better performance as balance as well . Finally , the Mini-PointConv is tested in a variety of indoor environments using laser radar and obtain a good indoor 3D point cloud recognition result .
Extensive experiments on challenging benchmarks show that our proposed model properly captures the local patterns to provide discriminative point set features . For the task of 3D scene semantic segmentation , our method outperforms the state-of-the-art on the challenging Stanford Large-Scale 3D Indoor Spaces Dataset(S3DIS ) by a large margin .
These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection . The extensive experiments on a pelvic CT image dataset , a multi-modal brain tumor dataset , and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation , respectively . Our experimental results also show that besides increasing the network complexity , raising the resolution of semantic feature maps can largely affect the overall model performance .
We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains . In this work we propose Shooting Labels , the first 3D labeling tool for dense 3D semantic segmentation which exploits Virtual Reality to render the labeling task as easy and fun as playing a video-game . Our tool allows for semantically labeling large scale environments very expeditiously , whatever the nature of the 3D data at hand ( e.g. pointclouds , mesh ) .
Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data , and how to smartly fuse information from frame to frame . We propose a novel fusion-aware 3D point convolution which operates directly on the geometric surface being reconstructed and exploits effectively the inter-frame correlation for high-quality 3D feature learning .
We also introduce local region based energies , overcoming the limitations of the original method . The feasibility of real-time 3D segmentation in challenging images using the proposed algorithm is demonstrated in liver tumor data . The segmentation accuracy is comparable to a recently proposed state-of-the-art algorithm , but with CPU times below 0.1s .
In the context of cancer delineation using positron emission tomography datasets , we present an innovative approach which purpose is to tackle the real-time , three-dimensional segmentation task in a full , or at least nearly full automatized way . The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset .
We present a novel method for the semantic segmentation of 3D data in large-scale urban environment . Our method significantly reduces the intensive labeling cost in previous works by automatically generating training data from the input data .
Due to the high complexity of spinal structures , the segmentation of 3D multiple spinal structures with consistently reliable and high accuracy is still a significant challenge . We developed and validated a relatively complete solution for the simultaneous 3D semantic segmentation of multiple spinal structures at the voxel level named as the S3egANet . Firstly , S3egANet explicitly solved the high variety and variability of complex 3D spinal structures through a multi-modality autoencoder module that was capable of extracting fine-grained structural information .
section : Acknowledgment : This work was funded by the Christian Doppler Laboratory for Semantic 3D Computer Vision . section :
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation Indoor Scene Understanding in 2.5/3D: A Survey SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM Conditional Random Fields as Recurrent Neural Networks Recurrent Pixel Embedding for Instance Grouping Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Unsupervised optimal phoneme segmentation: theory and experimental evaluation Texture segmentation using globally active contours model and Cauchy-Schwarz distance Ensemble Combination for Solving the Parameter Selection Problem in Image Segmentation Unsupervised image segmentation using wavelet-domain hidden Markov models Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization Nonlinear Unsupervised Clustering of Hyperspectral Images with Applications to Anomaly Detection and Active Learning Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images. Domain agnostic online semantic segmentation for multi-dimensional time series Semantic Component Analysis Semantic Instance Segmentation with a Discriminative Loss Function SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Dense 3D semantic mapping of indoor scenes from RGB-D images Indoor point cloud recognition with deep convolutional networks 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local Contextual Cues High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation Shooting Labels: 3D Semantic Labeling by Virtual Reality Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation Real-time region-based segmentation of 3D inhomogeneous objects in medical images Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography Semantic Segmentation of Large-Scale Urban 3D Data with Low Annotation Cost S3egANet: 3D Spinal Structures Segmentation via Adversarial Nets BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
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2015 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ICLR IPTA SSPR/SPR ICCV ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ICRA ISBI 2017 IEEE International Conference on Computer Vision (ICCV)
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