11.2.4.1 Depth Object Segmentation, Point Cloud Segmentation

Chapter Contents (Back)
Object Detection. Segmentation, Range. Object Segmentation. Point Cloud Segmentation. Segment the objects. More particularily:
See also Range and Color, RGB-D Segmentation and Analysis.
See also Depth Object Detection, 3D Object Detection.
See also Semantic Object Detection, 3D, Depth.

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

Liu, Z.Y.[Zheng-Yi], Wang, Y.[Yuan], Tan, Y.C.[Ya-Cheng], Li, W.[Wei], Xiao, Y.[Yun],
AGRFNet: Two-stage cross-modal and multi-level attention gated recurrent fusion network for RGB-D saliency detection,
SP:IC(104), 2022, pp. 116674.
Elsevier DOI 2204
Salient object detection, Gated recurrent unit, Attention mechanism, Cross-modal, Multi-level, RGB-D image 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.H.[Yu-Hao], Yang, Y.P.[Yan-Ping], 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.Y.[Han-Yun], 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

Huang, H.[Hao], Li, X.[Xiang], Wang, L.J.[Ling-Jing], Fang, Y.[Yi],
3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation,
3DV21(982-991)
IEEE DOI 2201
Training, Representation learning, Adaptation models, Solid modeling, Shape, Computational modeling BibRef

Li, X.Y.[Xing-Ye], Zhang, L.[Ling], Zhu, Z.G.[Zhi-Gang],
SnapshotNet: Self-supervised feature learning for point cloud data segmentation using minimal labeled data,
CVIU(216), 2022, pp. 103339.
Elsevier DOI 2202
Self-supervision, Point cloud, Semantic segmentation BibRef

Jing, W.P.[Wei-Peng], Zhang, W.J.[Wen-Jun], Li, L.H.[Lin-Hui], Di, D.L.[Dong-Lin], Chen, G.S.[Guang-Sheng], Wang, J.[Jian],
AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, F.[Fei], Zhuang, Y.[Yan], Zhang, H.[Hong], Gu, H.[Hong],
Real-Time 3-D Semantic Scene Parsing With LiDAR Sensors,
Cyber(52), No. 3, March 2022, pp. 1351-1363.
IEEE DOI 2203
Convolution, Semantics, Real-time systems, Laser radar, Tensile stress, Task analysis, 3-D convolutional neural network, sparse (ST) BibRef

Qiu, S.[Shi], Anwar, S.[Saeed], Barnes, N.[Nick],
Geometric Back-Projection Network for Point Cloud Classification,
MultMed(24), No. 2022, pp. 1943-1955.
IEEE DOI 2204
BibRef
Earlier:
Dense-Resolution Network for Point Cloud Classification and Segmentation,
WACV21(3812-3821)
IEEE DOI 2106
Feature extraction, Task analysis, Geometry, Visualization, Shape, Redundancy, Point Cloud Classification, 3D Deep Learning, Error-correcting Feedback. Training, Visualization, Adaptation models, Computational modeling BibRef

Li, Z.[Ziyu], Yao, Y.[Yuncong], Quan, Z.B.[Zhi-Bin], Xie, J.[Jin], Yang, W.K.[Wan-Kou],
Spatial information enhancement network for 3D object detection from point cloud,
PR(128), 2022, pp. 108684.
Elsevier DOI 2205
3D object detection, Autonomous vehicles, Point cloud, LiDAR sensor, 3D shape completion BibRef

Wang, M.M.[Ming-Ming], Chen, Q.[Qingkui], Fu, Z.B.[Zhi-Bing],
LSNet: Learned Sampling Network for 3D Object Detection from Point Clouds,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Zhao, C.[Chen], Yang, J.Q.[Jia-Qi], Xiong, X.[Xin], Zhu, A.F.[Ang-Fan], Cao, Z.G.[Zhi-Guo], Li, X.[Xin],
Rotation invariant point cloud analysis: Where local geometry meets global topology,
PR(127), 2022, pp. 108626.
Elsevier DOI 2205
Point cloud analysis, Rotation invariance, Deep learning, Classification, Segmentation BibRef

Xu, B.[Bo], Chen, Z.[Zhen], Zhu, Q.[Qing], Ge, X.M.[Xu-Ming], Huang, S.Z.[Sheng-Zhi], Zhang, Y.T.[Ye-Ting], Liu, T.Y.[Tian-Yang], Wu, D.[Di],
Geometrical Segmentation of Multi-Shape Point Clouds Based on Adaptive Shape Prediction and Hybrid Voting RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Wang, B.X.[Bing-Xu], Lan, J.H.[Jin-Hui], Gao, J.[Jiangjiang],
LiDAR Filtering in 3D Object Detection Based on Improved RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Cetinkaya, B.[Bedrettin], Kalkan, S.[Sinan], Akbas, E.[Emre],
Does depth estimation help object detection?,
IVC(122), 2022, pp. 104427.
Elsevier DOI 2205
Object detection, Depth estimation, RGB-D BibRef

Elich, C.[Cathrin], Oswald, M.R.[Martin R.], Pollefeys, M.[Marc], Stueckler, J.[Joerg],
Weakly supervised learning of multi-object 3D scene decompositions using deep shape priors,
CVIU(220), 2022, pp. 103440.
Elsevier DOI 2206
Multi-object 3D scene representation learning BibRef

Song, Z.J.[Zhan-Jie], Zhao, L.Q.[Lin-Qing], Zhou, J.[Jie],
Learning Hybrid Semantic Affinity for Point Cloud Segmentation,
CirSysVideo(32), No. 7, July 2022, pp. 4599-4612.
IEEE DOI 2207
Semantics, Point cloud compression, Image segmentation, Solid modeling, Task analysis, Learning systems, graph convolutional network BibRef

Tian, Y.L.[Yong-Lin], Huang, L.[Lichao], Yu, H.[Hui], Wu, X.B.[Xiang-Bin], Li, X.S.[Xue-Song], Wang, K.F.[Kun-Feng], Wang, Z.[Zilei], Wang, F.Y.[Fei-Yue],
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds,
ITS(23), No. 8, August 2022, pp. 10773-10785.
IEEE DOI 2208
Feature extraction, Convolution, Proposals, Kernel, Laser radar, Semantics, Point clouds, 3D detection, dynamic network, context features BibRef

Ouyang, Z.C.[Zhen-Chao], Dong, X.Y.[Xiao-Yun], Cui, J.[Jiahe], Niu, J.W.[Jian-Wei], Guizani, M.[Mohsen],
PV-EncoNet: Fast Object Detection Based on Colored Point Cloud,
ITS(23), No. 8, August 2022, pp. 12439-12450.
IEEE DOI 2208
Encoding, Object detection, Solid modeling, Feature extraction, Data models, Convolution, Multi-Sensor fusion, point cloud, camera, self-driving BibRef

Ma, R.Q.[Rui-Qi], Chen, C.[Chi], Yang, B.[Bisheng], Li, D.R.[De-Ren], Wang, H.P.[Hai-Ping], Cong, Y.Z.[Yang-Zi], Hu, Z.[Zongtian],
CG-SSD: Corner guided single stage 3D object detection from LiDAR point cloud,
PandRS(191), 2022, pp. 33-48.
Elsevier DOI 2208
LiDAR, Point clouds, 3D object detection, Deep learning BibRef

Zhao, Y.H.[Yong-Heng], Fang, G.C.[Guang-Chi], Guo, Y.L.[Yu-Lan], Guibas, L.J.[Leonidas J.], Tombari, F.[Federico], Birdal, T.[Tolga],
3DPointCaps++: Learning 3D Representations with Capsule Networks,
IJCV(130), No. 9, September 2022, pp. 2321-2336.
Springer DOI 2208
BibRef

Zhu, X.G.[Xin-Ge], Zhou, H.[Hui], Wang, T.[Tai], Hong, F.Z.[Fang-Zhou], Li, W.[Wei], Ma, Y.X.[Yue-Xin], Li, H.S.[Hong-Sheng], Yang, R.G.[Rui-Gang], Lin, D.[Dahua],
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception,
PAMI(44), No. 10, October 2022, pp. 6807-6822.
IEEE DOI 2209
BibRef
Earlier: A1, A2, A3, A4, A6, A5, A7, A9:
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation,
CVPR21(9934-9943)
IEEE DOI 2111
Laser radar, Convolution, Feature extraction, Gold, Task analysis, Solid modeling, Cylindrical partition, asymmetrical convolution, point cloud panoptic segmentation. Solid modeling, Network topology, Interference, Encoding BibRef

Cai, Q.[Qi], Pan, Y.[Yingwei], Yao, T.[Ting], Mei, T.[Tao],
3D Cascade RCNN: High Quality Object Detection in Point Clouds,
IP(31), 2022, pp. 5706-5719.
IEEE DOI 2209
Proposals, Object detection, Point cloud compression, Detectors, Training, Task analysis, Point cloud, 3D object detection, sample re-weighting BibRef

Zhu, L.[Lei], Chen, W.N.[Wei-Nan], Lin, X.[Xubin], He, L.[Li], Guan, Y.[Yisheng],
Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation,
SPLetters(29), 2022, pp. 1868-1872.
IEEE DOI 2209
Point cloud compression, Shape, Task analysis, Geometry, Sampling methods, Convolution, Curvature variation, point cloud BibRef

Zheng, Y.[Yu], Xu, X.[Xiuwei], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
PointRas: Uncertainty-Aware Multi-Resolution Learning for Point Cloud Segmentation,
IP(31), 2022, pp. 6002-6016.
IEEE DOI 2209
Point cloud compression, Decoding, Signal resolution, Shape, Interpolation, Semantics, Point cloud segmentation, contextual learning BibRef

Honti, R.[Richard], Erdélyi, J.[Ján], Kopácik, A.[Alojz],
Semi-Automated Segmentation of Geometric Shapes from Point Clouds,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef


Mohammadi, S.S.[Seyed Saber], Wang, Y.M.[Yi-Ming], Taiana, M.[Matteo], Morerio, P.[Pietro], del Bue, A.[Alessio],
SVP-Classifier: Single-View Point Cloud Data Classifier with Multi-view Hallucination,
CIAP22(II:15-26).
Springer DOI 2205
BibRef

Chen, Y.[Ye], Liu, J.X.[Jin-Xian], Ni, B.B.[Bing-Bing], Wang, H.[Hang], Yang, J.C.[Jian-Cheng], Liu, N.[Ning], Li, T.[Teng], Tian, Q.[Qi],
Shape Self-Correction for Unsupervised Point Cloud Understanding,
ICCV21(8362-8371)
IEEE DOI 2203
Point cloud compression, Deep learning, Analytical models, Shape, Pipelines, Feature extraction, Representation learning BibRef

Nie, X.[Xing], Liu, Y.C.[Yong-Cheng], Chen, S.H.[Shao-Hong], Chang, J.L.[Jian-Long], Huo, C.L.[Chun-Lei], Meng, G.F.[Gao-Feng], Tian, Q.[Qi], Hu, W.M.[Wei-Ming], Pan, C.H.[Chun-Hong],
Differentiable Convolution Search for Point Cloud Processing,
ICCV21(7417-7426)
IEEE DOI 2203
To enable CNN. Point cloud compression, Directed acyclic graph, Convolution, Shape, Heuristic algorithms, Computer architecture, Segmentation, Representation learning BibRef

Lę, E.T.[Eric-Tuan], Sung, M.[Minhyuk], Ceylan, D.[Duygu], Mech, R.[Radomir], Boubekeur, T.[Tamy], Mitra, N.J.[Niloy J.],
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds,
ICCV21(7438-7446)
IEEE DOI 2203
Point cloud compression, Codes, Fitting, Merging, Reverse engineering, Pipelines, Segmentation, grouping and shape, BibRef

Yang, J.Y.[Ju-Young], Ahn, P.[Pyunghwan], Kim, D.Y.[Do-Yeon], Lee, H.[Haeil], Kim, J.[Junmo],
Progressive Seed Generation Auto-Encoder for Unsupervised Point Cloud Learning,
ICCV21(6393-6402)
IEEE DOI 2203
Point cloud compression, Annotations, Focusing, Computer architecture, Feature extraction, Stereo, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Ye, S.[Shuquan], Chen, D.D.[Dong-Dong], Han, S.[Songfang], Liao, J.[Jing],
Learning with Noisy Labels for Robust Point Cloud Segmentation,
ICCV21(6423-6432)
IEEE DOI 2203
Point cloud compression, Image segmentation, Correlation, Upper bound, Lead, Robustness, Optimization and learning methods, 3D from multiview and other sensors BibRef

Venkatesh, R.[Rahul], Karmali, T.[Tejan], Sharma, S.[Sarthak], Ghosh, A.[Aurobrata], Babu, R.V.[R. Venkatesh], Jeni, L.A.[Lászlo A.], Singh, M.[Maneesh],
Deep Implicit Surface Point Prediction Networks,
ICCV21(12633-12642)
IEEE DOI 2203
Point cloud compression, Solid modeling, Shape, Computational modeling, Predictive models, 3D from multiview and other sensors BibRef

Hui, L.[Le], Yuan, J.[Jia], Cheng, M.[Mingmei], Xie, J.[Jin], Zhang, X.Y.[Xiao-Ya], Yang, J.[Jian],
Superpoint Network for Point Cloud Oversegmentation,
ICCV21(5490-5499)
IEEE DOI 2203
Point cloud compression, Codes, Semantics, Stereo, 3D from multiview and other sensors, BibRef

Yan, S.M.[Si-Ming], Yang, Z.P.[Zhen-Pei], Ma, C.Y.[Chong-Yang], Huang, H.B.[Hai-Bin], Vouga, E.[Etienne], Huang, Q.X.[Qi-Xing],
HPNet: Deep Primitive Segmentation Using Hybrid Representations,
ICCV21(2733-2742)
IEEE DOI 2203
Point cloud compression, Shape, Semantics, Performance gain, Benchmark testing, Detection and localization in 2D and 3D, grouping and shape BibRef

Zou, L.[Longkun], Tang, H.[Hui], Chen, K.[Ke], Jia, K.[Kui],
Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds,
ICCV21(6383-6392)
IEEE DOI 2203
Point cloud compression, Geometry, Representation learning, Location awareness, Shape, Semantics, Stereo, BibRef

Mao, J.[Jiageng], Niu, M.Z.[Min-Zhe], Bai, H.Y.[Hao-Yue], Liang, X.D.[Xiao-Dan], Xu, H.[Hang], Xu, C.J.[Chun-Jing],
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection,
ICCV21(2703-2712)
IEEE DOI 2203
Point cloud compression, Solid modeling, Adaptation models, Focusing, Object detection, Vision for robotics and autonomous vehicles BibRef

Xu, J.Y.[Jian-Yun], Zhang, R.X.[Rui-Xiang], Dou, J.[Jian], Zhu, Y.[Yushi], Sun, J.[Jie], Pu, S.L.[Shi-Liang],
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation,
ICCV21(16004-16013)
IEEE DOI 2203
Point cloud compression, Quantization (signal), Laser radar, Image resolution, Logic gates, Solids, 3D from multiview and other sensors BibRef

Yang, C.K.[Cheng-Kun], Chuang, Y.Y.[Yung-Yu], Lin, Y.Y.[Yen-Yu],
Unsupervised Point Cloud Object Co-segmentation by Co-contrastive Learning and Mutual Attention Sampling,
ICCV21(7315-7324)
IEEE DOI 2203
Point cloud compression, Deep learning, Correlation, Annotations, Neural networks, Segmentation, grouping and shape, 3D from multiview and other sensors BibRef

Ye, M.S.[Mao-Sheng], Xu, S.[Shuangjie], Cao, T.[Tongyi], Chen, Q.F.[Qi-Feng],
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation,
ICCV21(7427-7436)
IEEE DOI 2203
Point cloud compression, Representation learning, Degradation, Runtime, Costs, Feature extraction, Segmentation, grouping and shape, Vision for robotics and autonomous vehicles BibRef

Wei, Y.M.[Yi-Min], Liu, H.[Hao], Xie, T.T.[Ting-Ting], Ke, Q.H.[Qiu-Hong], Guo, Y.L.[Yu-Lan],
Spatial-Temporal Transformer for 3D Point Cloud Sequences,
WACV22(657-666)
IEEE DOI 2202
Point cloud compression, Solid modeling, Aggregates, Semantics, Grouping and Shape BibRef

Shakibajahromi, B.[Bahareh], Shayestehmanesh, S.[Saeed], Schwartz, D.[Daniel], Shokoufandeh, A.[Ali],
HyNet: 3D Segmentation Using Hybrid Graph Networks,
3DV21(805-814)
IEEE DOI 2201
Representation learning, Deep learning, Solid modeling, Shape, Biological system modeling, Focusing BibRef

Cen, J.[Jun], Yun, P.[Peng], Cai, J.[Junhao], Wang, M.Y.[Michael Yu], Liu, M.[Ming],
Open-set 3D Object Detection,
3DV21(869-878)
IEEE DOI 2201
Measurement, Point cloud compression, Upper bound, Laser radar, Object detection, Open systems BibRef

Brodeur, T.[Tristan], Ali Akbarpour, H.[Hadi], Suddarth, S.[Steve],
Point Cloud Object Segmentation Using Multi Elevation-Layer 2D Bounding-Boxes,
WAAMI21(3910-3918)
IEEE DOI 2112
Measurement, Octrees, Merging, Object segmentation 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.W.[Hai-Wei], Liu, S.C.[Shi-Chen], Chen, W.K.[Wei-Kai], 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

Peng, X.D.[Xi-Dong], Zhu, X.G.[Xin-Ge], Wang, T.[Tai], Ma, Y.X.[Yue-Xin],
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation,
WACV22(225-234)
IEEE DOI 2202
Costs, Laser radar, Computational modeling, Estimation, Detectors, Scene Understanding BibRef

Cheng, B.[Bowen], Sheng, L.[Lu], Shi, S.S.[Shao-Shuai], 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

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.], Laganičre, 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.Y.[Zhi-Yu], Gu, J.Q.[Jia-Qi],
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., Lathuiličre, 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
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Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Depth Object Detection, 3D Object Detection .


Last update:Sep 28, 2022 at 16:10:08