11.2.4.1 Depth Object Segmentation, Point Cloud Segmentation

Zhang, J.X.[Ji-Xian], Lin, X.G.[Xiang-Guo],
Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification,
PandRS(81), No. 1, July 2013, pp. 44-59.
Elsevier DOI 1306
Airborne LiDAR; Filtering; Progressive TIN densification; Point cloud segmentation; Segmentation using smoothness constraint BibRef

Vo, A.V.[Anh-Vu], Truong-Hong, L.[Linh], Laefer, D.F.[Debra F.], Bertolotto, M.[Michela],
Octree-based region growing for point cloud segmentation,
PandRS(104), No. 1, 2015, pp. 88-100.
Elsevier DOI 1505
Segmentation BibRef

Ben-Shabat, Y.[Yizhak], Avraham, T.[Tamar], Lindenbaum, M.[Michael], Fischer, A.[Anath],
Graph based over-segmentation methods for 3D point clouds,
CVIU(174), 2018, pp. 12-23.
Elsevier DOI 1812
3D point cloud over-segmentation, 3D point cloud segmentation, Super-points, Grouping BibRef

Zhao, B.F.[Bu-Fan], Hua, X.H.[Xiang-Hong], Yu, K.G.[Ke-Gen], Xuan, W.[Wei], Chen, X.J.[Xi-Jiang], Tao, W.Y.[Wu-Yong],
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting,
GeoRS(58), No. 11, November 2020, pp. 7890-7907.
IEEE DOI 2011
Machine learning, Feature extraction, Convolution, Laser modes, Shape, Fitting, 3-D point cloud, segmentation BibRef

Zhang, S., Cui, S., Ding, Z.,
Hypergraph Spectral Clustering for Point Cloud Segmentation,
SPLetters(27), 2020, pp. 1655-1659.
IEEE DOI 1806
Tensile stress, Frequency estimation, Estimation, Covariance matrices, Laplace equations, Hypergraph, spectral clustering BibRef

Feng, M.T.[Ming-Tao], Gilani, S.Z.[Syed Zulqarnain], Wang, Y.N.[Yao-Nan], Zhang, L.[Liang], Mian, A.[Ajmal],
Relation Graph Network for 3D Object Detection in Point Clouds,
IP(30), 2021, pp. 92-107.
IEEE DOI 2011
Proposals, Object detection, Feature extraction, Laser radar, deep learning BibRef

Lei, H.[Huan], Akhtar, N.[Naveed], Mian, A.[Ajmal],
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds,
PAMI(43), No. 10, October 2021, pp. 3664-3680.
IEEE DOI 2109
BibRef
Earlier:
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel,
CVPR20(11608-11617)
IEEE DOI 2008
BibRef
Earlier:
Octree Guided CNN With Spherical Kernels for 3D Point Clouds,
CVPR19(9623-9632).
IEEE DOI 2002
Kernel, Convolution, Neural networks, Feature extraction, Semantics, Computer architecture, semantic segmentation. Convolutional codes, Integrated circuits, Robustness BibRef

Hsu, P.H.[Pai-Hui], Zhuang, Z.Y.[Zong-Yi],
Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Li, X.[Xiaohan], Chen, L.[Lu], Li, S.[Shuang], Zhou, X.[Xiang],
Depth segmentation in real-world scenes based on U-V disparity analysis,
JVCIR(73), 2020, pp. 102920.
Elsevier DOI 2012
Depth scene segmentation, U-V disparity map, Projection characteristics analysis, Object detection, RANSAC algorithm BibRef

Guarda, A.F.R.[André F. R.], Rodrigues, N.M.M.[Nuno M. M.], Pereira, F.[Fernando],
Constant Size Point Cloud Clustering: A Compact, Non-Overlapping Solution,
MultMed(23), 2021, pp. 77-91.
IEEE DOI 2012
Clustering algorithms, Clustering methods, Transform coding, Encoding, Image segmentation, Complexity theory, Point cloud, non-overlapping BibRef

Guarda, A.F.R.[André F. R.], Rodrigues, N.M.M.[Nuno M. M.], Pereira, F.[Fernando],
Neighborhood Adaptive Loss Function for Deep Learning-Based Point Cloud Coding With Implicit and Explicit Quantization,
MultMedMag(28), No. 3, July 2021, pp. 107-116.
IEEE DOI 2109
Encoding, Deep learning, Distortion, Geometry, Machine learning, Image coding, Point cloud coding, explicit quantization BibRef

Tian, Y.F.[Yi-Fei], Chen, L.[Long], Song, W.[Wei], Sung, Y.S.[Yun-Sick], Woo, S.C.[Sang-Chul],
DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Su, F.[Fei], Zhu, H.H.[Hai-Hong], Chen, T.Y.[Tao-Yi], Li, L.[Lin], Yang, F.[Fan], Peng, H.X.[Hui-Xiang], Tang, L.[Lei], Zuo, X.K.[Xin-Kai], Liang, Y.F.[Yi-Fan], Ying, S.[Shen],
An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds,
PandRS(172), 2021, pp. 114-131.
Elsevier DOI 2101
Point cloud, Object classification, Functional part, Graph matching, Super-graph, Graph similarity BibRef

Wang, W.M.[Wei-Ming], You, Y.[Yang], Liu, W.[Wenhai], Lu, C.[Cewu],
Point cloud classification with deep normalized Reeb graph convolution,
IVC(106), 2021, pp. 104092.
Elsevier DOI 2102
Reeb graph, Point cloud, Graph normalization BibRef

Ma, L., Li, Y., Li, J., Tan, W., Yu, Y., Chapman, M.A.,
Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments,
ITS(22), No. 2, February 2021, pp. 821-836.
IEEE DOI 2102
Feature extraction, Semantics, Shape, Solid modeling, Neural networks, Roads, Point clouds, k-nearest neighbor BibRef

Geng, X.X.[Xiao-Xiao], Ji, S.P.[Shun-Ping], Lu, M.[Meng], Zhao, L.L.[Ling-Li],
Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Luo, N.[Nan], Yu, H.Q.[Hong-Quan], Huo, Z.F.[Zhen-Feng], Liu, J.H.[Jin-Hui], Wang, Q.[Quan], Xu, Y.[Ying], Gao, Y.[Yun],
KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Li, G.Y.[Gong-Yang], Liu, Z.[Zhi], Chen, M.Y.[Min-Yu], Bai, Z.[Zhen], Lin, W.S.[Wei-Si], Ling, H.B.[Hai-Bin],
Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection,
IP(30), 2021, pp. 3528-3542.
IEEE DOI 2103
Object detection, Feature extraction, Color, Visualization, Task analysis, Computer architecture, Stereo image processing, alternate interaction BibRef

Li, G.Y.[Gong-Yang], Liu, Z.[Zhi], Ye, L.W.[Lin-Wei], Wang, Y.[Yang], Ling, H.B.[Hai-Bin],
Cross-modal Weighting Network for RGB-D Salient Object Detection,
ECCV20(XVII:665-681).
Springer DOI 2011
BibRef

Zhang, X. .L.[Xin- Liang], Fu, C.L.[Chen-Lin], Zhao, Y.J.[Yun-Ji], Xu, X.Z.[Xiao-Zhuo],
Hybrid feature CNN model for point cloud classification and segmentation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4086-4091.
DOI Link 2103
BibRef

Wang, Q.[Qi], Chen, J.[Jian], Deng, J.Q.[Jian-Qiang], Zhang, X.F.[Xin-Fang],
3D-CenterNet: 3D object detection network for point clouds with center estimation priority,
PR(115), 2021, pp. 107884.
Elsevier DOI 2104
3D object detection, Point cloud, Deep learning BibRef

Li, D.W.[Da-Wei], Shi, G.L.[Guo-Liang], Wu, Y.[Yuhao], Yang, Y.[Yanping], Zhao, M.B.[Ming-Bo],
Multi-Scale Neighborhood Feature Extraction and Aggregation for Point Cloud Segmentation,
CirSysVideo(31), No. 6, June 2021, pp. 2175-2191.
IEEE DOI 2106
Feature extraction, Semantics, Image segmentation, Data mining, point cloud segmentation BibRef

Chen, C.F.[Chao-Fan], Qian, S.S.[Sheng-Sheng], Fang, Q.[Quan], Xu, C.S.[Chang-Sheng],
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation,
MultMed(23), 2021, pp. 2335-2346.
IEEE DOI 2108
Feature extraction, Task analysis, Layout, Logic gates, Machine learning, gated graph attention network BibRef

Zhu, J.F.[Jian-Feng], Sui, L.C.[Li-Chun], Zang, Y.[Yufu], Zheng, H.[He], Jiang, W.[Wei], Zhong, M.[Mianqing], Ma, F.[Fei],
Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Brown, K.[Kyle], Bourbakis, N.[Nikolaos],
Curve and Surface Fitting Techniques in Computer Vision,
IJIG(21), No. 4, October 2021 2021, pp. 2150041.
DOI Link 2110
BibRef

Guo, Y.L.[Yu-Lan], Wang, H.[Hanyun], Hu, Q.Y.[Qing-Yong], Liu, H.[Hao], Liu, L.[Li], Bennamoun, M.[Mohammed],
Deep Learning for 3D Point Clouds: A Survey,
PAMI(43), No. 12, December 2021, pp. 4338-4364.
IEEE DOI 2112
Solid modeling, Deep learning, Object detection, Laser radar, Task analysis, Sensors, Deep learning, part segmentation BibRef

Yang, F.[Fei], Davoine, F.[Franck], Wang, H.[Huan], Jin, Z.[Zhong],
Continuous conditional random field convolution for point cloud segmentation,
PR(122), 2022, pp. 108357.
Elsevier DOI 2112
Point cloud segmentation, Conditional random fields, Message passing, Graph convolution, Mean-field approximation BibRef

Zhang, J.[Jing], Wang, J.J.[Jia-Jun], Xu, D.[Da], Li, Y.S.[Yun-Song],
HCNET: A Point Cloud Object Detection Network Based on Height and Channel Attention,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Hu, W.[Wenbo], Zhao, H.S.[Heng-Shuang], Jiang, L.[Li], Jia, J.Y.[Jia-Ya], Wong, T.T.[Tien-Tsin],
Bidirectional Projection Network for Cross Dimension Scene Understanding,
CVPR21(14368-14377)
IEEE DOI 2111
Geometry, Visualization, Semantics, Benchmark testing, Image representation BibRef

Huang, C.[Chao], Cao, Z.J.[Zhang-Jie], Wang, Y.[Yunbo], Wang, J.M.[Jian-Min], Long, M.S.[Ming-Sheng],
MetaSets: Meta-Learning on Point Sets for Generalizable Representations,
CVPR21(8859-8868)
IEEE DOI 2111
Geometry, Training, Deep learning, Solid modeling, Benchmark testing BibRef

Yi, L.[Li], Gong, B.Q.[Bo-Qing], Funkhouser, T.[Thomas],
Complete amp; Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds,
CVPR21(15358-15368)
IEEE DOI 2111
Training, Laser radar, Semantics, Transforms, Manuals, Sensors BibRef

Gong, J.Y.[Jing-Yu], Xu, J.C.[Jia-Chen], Tan, X.[Xin], Song, H.C.[Hai-Chuan], Qu, Y.Y.[Yan-Yun], Xie, Y.[Yuan], Ma, L.Z.[Li-Zhuang],
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning,
CVPR21(11668-11677)
IEEE DOI 2111
Codes, Semantics, Neural networks, Benchmark testing, Cognition, Entropy BibRef

Lu, T.[Tao], Wang, L.M.[Li-Min], Wu, G.S.[Gang-Shan],
CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation,
CVPR21(11688-11697)
IEEE DOI 2111
Aggregates, Semantics, Computer architecture, Pattern recognition BibRef

Fan, S.Q.[Si-Qi], Dong, Q.L.[Qiu-Lei], Zhu, F.[Fenghua], Lv, Y.S.[Yi-Sheng], Ye, P.J.[Pei-Jun], Wang, F.Y.[Fei-Yue],
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation,
CVPR21(14499-14508)
IEEE DOI 2111
Semantics, Computer architecture, Network architecture, Pattern recognition BibRef

Chen, H.[Haiwei], Liu, S.[Shichen], Chen, W.[Weikai], Li, H.[Hao], Hill, R.[Randall],
Equivariant Point Network for 3D Point Cloud Analysis,
CVPR21(14509-14518)
IEEE DOI 2111
Convolutional codes, Visualization, Shape, Convolution, Computational modeling BibRef

Zheng, W.[Wu], Tang, W.L.[Wei-Liang], Jiang, L.[Li], Fu, C.W.[Chi-Wing],
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud,
CVPR21(14489-14498)
IEEE DOI 2111
Training, Matched filters, Shape, Detectors, Object detection BibRef

Zhu, X.G.[Xin-Ge], Zhou, H.[Hui], Wang, T.[Tai], Hong, F.Z.[Fang-Zhou], Ma, Y.[Yuexin], Li, W.[Wei], Li, H.[Hongsheng], Lin, D.[Dahua],
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation,
CVPR21(9934-9943)
IEEE DOI 2111
Solid modeling, Laser radar, Convolution, Network topology, Interference, Encoding BibRef

Cheng, B.[Bowen], Sheng, L.[Lu], Shi, S.[Shaoshuai], Yang, M.[Ming], Xu, D.[Dong],
Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds,
CVPR21(8959-8968)
IEEE DOI 2111
Location awareness, Visualization, Object detection, Feature extraction, Pattern recognition, Proposals BibRef

Qi, C.R.[Charles R.], Zhou, Y.[Yin], Najibi, M.[Mahyar], Sun, P.[Pei], Vo, K.[Khoa], Deng, B.[Boyang], Anguelov, D.[Dragomir],
Offboard 3D Object Detection from Point Cloud Sequences,
CVPR21(6130-6140)
IEEE DOI 2111
Training, Pipelines, Detectors, Object detection, Semisupervised learning, Real-time systems BibRef

Tian, H.[Hao], Chen, Y.T.[Yun-Tao], Dai, J.[Jifeng], Zhang, Z.X.[Zhao-Xiang], Zhu, X.[Xizhou],
Unsupervised Object Detection with LiDAR Clues,
CVPR21(5958-5968)
IEEE DOI 2111
Training, Location awareness, Image segmentation, Laser radar, Annotations, Object detection BibRef

Li, Z.C.[Zhi-Chao], Wang, F.[Feng], Wang, N.[Naiyan],
LiDAR R-CNN: An Efficient and Universal 3D Object Detector,
CVPR21(7542-7551)
IEEE DOI 2111
Laser radar, Costs, Codes, Detectors, Real-time systems BibRef

Fang, J.[Jin], Zuo, X.X.[Xin-Xin], Zhou, D.[Dingfu], Jin, S.Z.[Sheng-Ze], Wang, S.[Sen], Zhang, L.J.[Liang-Jun],
LiDAR-Aug: A General Rendering-based Augmentation Framework for 3D Object Detection,
CVPR21(4708-4718)
IEEE DOI 2111
Training, Laser radar, Neural networks, Training data, Object detection, Detectors BibRef

Aguilar, C.[Camilo], Comer, M.[Mary], Hanhan, I.[Imad], Agyei, R.[Ronald], Sangid, M.[Michael],
3D Fiber Segmentation with Deep Center Regression and Geometric Clustering,
CVMI21(3741-3749)
IEEE DOI 2109

WWW Link. Geometry, Training, Image color analysis, Shape, Microscopy, Neural networks BibRef

Xiao, C.X.[Chen-Xi], Wachs, J.[Juan],
Triangle-Net: Towards Robustness in Point Cloud Learning,
WACV21(826-835)
IEEE DOI 2106
Service robots, Surveillance, Neural networks, Feature extraction, Robustness BibRef

Qiu, S.[Shi], Anwar, S.[Saeed], Barnes, N.[Nick],
Dense-Resolution Network for Point Cloud Classification and Segmentation,
WACV21(3812-3821)
IEEE DOI 2106
Training, Visualization, Adaptation models, Computational modeling BibRef

Yang, Y.R.[Yi-Rong], Fan, B.[Bin], Liu, Y.C.[Yong-Cheng], Lin, H.[Hua], Zhang, J.Y.[Ji-Yong], Liu, X.[Xin], Cai, X.Y.[Xin-Yu], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Deep Space Probing for Point Cloud Analysis,
ICPR21(10235-10242)
IEEE DOI 2105
Geometry, Convolution, Neural networks, Benchmark testing, Convolutional neural networks BibRef

Lin, H.[Hua], Fan, B.[Bin], Liu, Y.C.[Yong-Cheng], Yang, Y.R.[Yi-Rong], Pan, Z.[Zheng], Shi, J.B.[Jian-Bo], Pan, C.H.[Chun-Hong], Xie, H.W.[Hui-Wen],
PointSpherical: Deep Shape Context for Point Cloud Learning in Spherical Coordinates,
ICPR21(10266-10273)
IEEE DOI 2105
Solid modeling, Shape, Convolution, Semantics, Feature extraction BibRef

Alliegro, A.[Antonio], Boscaini, D.[Davide], Tommasi, T.[Tatiana],
Joint Supervised and Self-Supervised Learning for 3D Real World Challenges,
ICPR21(6718-6725)
IEEE DOI 2105
Solid modeling, Shape, Transfer learning, Supervised learning, Intelligent agents BibRef

Pan, Y.[Yunyi], Gan, Y.[Yuan], Liu, K.[Kun], Zhang, Y.[Yan],
Progressive Scene Segmentation Based on Self-Attention Mechanism,
ICPR21(3985-3992)
IEEE DOI 2105
Convolution, Semantics, Benchmark testing, Decoding, Task analysis, 3D Scene Understanding BibRef

Zhong, M.[Min], Zeng, G.[Gang],
Enhanced Vote Network for 3D Object Detection in Point Clouds,
ICPR21(6624-6631)
IEEE DOI 2105
Aggregates, Face recognition, Semantics, Object detection, Benchmark testing, Feature extraction BibRef

Demilew, S.S.[Selameab S.], Aghdam, H.H.[Hamed H.], Laganière, R.[Robert], Petriu, E.M.[Emil M.],
FA3D: Fast and Accurate 3d Object Detection,
ISVC20(I:397-409).
Springer DOI 2103
BibRef

Krishna, O.[Onkar], Irie, G.[Go], Wu, X.[Xiaomeng], Kawanishi, T.[Takahito], Kashino, K.[Kunio],
Adaptive Spotting: Deep Reinforcement Object Search in 3d Point Clouds,
ACCV20(III:257-272).
Springer DOI 2103
BibRef

Zhang, Y.[Yi], Ye, Y.[Yuwen], Xiang, Z.[Zhiyu], Gu, J.[Jiaqi],
Sdp-net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3d Point Clouds,
ACCV20(I:140-157).
Springer DOI 2103
BibRef

Liu, X., Cao, J., Bi, Q., Wang, J., Shi, B., Wei, Y.,
Dense Point Diffusion for 3D Object Detection,
3DV20(762-770)
IEEE DOI 2102
Convolution, Object detection, Feature extraction, Task analysis, Quantization (signal) BibRef

Saltori, C., Lathuilière, S., Sebe, N., Ricci, E., Galasso, F.,
SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection,
3DV20(771-780)
IEEE DOI 2102
Annotations, Detectors, Adaptation models, Laser radar, Target tracking, LiDAR data BibRef

Krispel, G., Opitz, M., Waltner, G., Possegger, H., Bischof, H.,
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data,
WACV20(1863-1872)
IEEE DOI 2006
Laser radar, Task analysis, Sensors, Laser beams, Fuses, Image segmentation BibRef

Barrile, V., Candela, G., Fotia, A.,
Point Cloud Segmentation Using Image Processing Techniques For Structural Analysis,
GEORES19(187-193).
DOI Link 1912
BibRef

Wang, W., Yu, R., Huang, Q., Neumann, U.,
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation,
CVPR18(2569-2578)
IEEE DOI 1812
Proposals, Image segmentation, Semantics, Feature extraction BibRef

Sharma, G.[Gopal], Liu, D.[Difan], Maji, S.[Subhransu], Kalogerakis, E.[Evangelos], Chaudhuri, S.[Siddhartha], Mech, R.[Radomír],
Parsenet: A Parametric Surface Fitting Network for 3d Point Clouds,
ECCV20(VII:261-276).
Springer DOI 2011
BibRef

Honma, R., Date, H., Kanai, S.,
MLS Point Cloud Segmentation Based On Feature Points of Scanlines,
Laser19(1007-1013).
DOI Link 1912
BibRef

Zhong, Z., Zhang, C., Liu, Y., Wu, Y.,
VIASEG: Visual Information Assisted Lightweight Point Cloud Segmentation,
ICIP19(1500-1504)
IEEE DOI 1910
Point Cloud Segmentation, Cross-modality Fusion, Fully Convolutional Residual Network BibRef

Walczak, J.[Jakub], Wojciechowski, A.[Adam],
Clustering Quality Measures for Point Cloud Segmentation Tasks,
ICCVG18(173-186).
Springer DOI 1810
BibRef

Kuçak, R.A., Özdemir, E., Erol, S.,
The Segmentation of Point Clouds with K-means and ANN (Artifical Neural Network),
Hannover17(595-598).
DOI Link 1805
BibRef

Lam, J.[Joseph], Greenspan, M.[Michael],
On the Repeatability of 3D Point Cloud Segmentation Based on Interest Points,
CRV12(9-16).
IEEE DOI 1207
BibRef

Akman, O.[Oytun], Bayramoglu, N.[Neslihan], Alatan, A.A.[A. Aydin], Jonker, P.P.[Pieter P.],
Utilization of spatial information for point cloud segmentation,
3DTV10(1-4).
IEEE DOI 1006
BibRef

Sedlacek, D.[David], Zara, J.[Jiri],
Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction,
ISVC09(II: 218-227).
Springer DOI 0911
BibRef

Zhan, Q.M.[Qing-Ming], Liang, Y.B.[Yu-Bin], Xiao, Y.H.[Ying-Hui],
Color-Based Segmentation of Point Clouds,
Laser09(248). 0909
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Depth Object Detection, 3D Object Detection .