11.2.4.1 Depth Object Segmentation, Point Cloud Segmentation

Chapter Contents (Back)
Object Detection. Segmentation, Range. Object Segmentation. Point Cloud Segmentation.
See also Semi-Supervised Object Detection, 3D Object Detection. 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.

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Airborne LiDAR; Filtering; Progressive TIN densification; Point cloud segmentation; Segmentation using smoothness constraint BibRef

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3D point cloud over-segmentation, 3D point cloud segmentation, Super-points, Grouping BibRef

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IEEE DOI 2011
Machine learning, Feature extraction, Convolution, Laser modes, Shape, Fitting, 3-D point cloud, segmentation BibRef

Zhang, S., Cui, S., Ding, Z.,
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IEEE DOI 1806
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Feng, M.T.[Ming-Tao], Gilani, S.Z.[Syed Zulqarnain], Wang, Y.N.[Yao-Nan], Zhang, L.[Liang], Mian, A.[Ajmal],
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IEEE DOI 2011
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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, 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,
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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

Guarda, A.F.R.[André F. R.], Rodrigues, N.M.M.[Nuno M. M.], Pereira, F.[Fernando],
Point Cloud Geometry and Color Coding in a Learning-Based Ecosystem for JPEG Coding Standards,
ICIP23(2585-2589)
IEEE DOI 2312
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Ruivo, M.[Manuel], Guarda, A.F.R.[André F. R.], Pereira, F.[Fernando],
Learning-Based Rate Control for Learning-Based Point Cloud Geometry Coding,
ICIP23(251-255)
IEEE DOI 2312
BibRef

Seleem, A.[Abdelrahman], Guarda, A.F.R.[André F. R.], Rodrigues, N.M.M.[Nuno M. M.], Pereira, F.[Fernando],
Deep Learning-Based Compressed Domain Point Cloud Classification,
ICIP23(2620-2624)
IEEE DOI 2312
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

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, 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],
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ECCV20(XVII:665-681).
Springer DOI 2011
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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
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Brown, K.[Kyle], Bourbakis, N.[Nikolaos],
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IJIG(21), No. 4, October 2021 2021, pp. 2150041.
DOI Link 2110
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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

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

Li, Z.Y.[Zi-Yu], Yao, Y.C.[Yun-Cong], 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.K.[Qing-Kui], 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.C.[Li-Chao], 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.S.[Bi-Sheng], Li, D.R.[De-Ren], Wang, H.P.[Hai-Ping], Cong, Y.Z.[Yang-Zi], Hu, Z.T.[Zong-Tian],
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.W.[Ying-Wei], 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

Zheng, Y.[Yu], Xu, X.W.[Xiu-Wei], 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

Xu, X.W.[Xiu-Wei], Wang, Z.W.[Zi-Wei], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Back to Reality: Learning Data-Efficient 3D Object Detector with Shape Guidance,
PAMI(46), No. 2, February 2024, pp. 1165-1180.
IEEE DOI 2401
Object detection, Shape, Point cloud compression BibRef

Zheng, Y.[Yu], Duan, Y.[Yueqi], Lu, J.W.[Ji-Wen], Zhou, J.[Jie], Tian, Q.[Qi],
HyperDet3D: Learning a Scene-conditioned 3D Object Detector,
CVPR22(5575-5584)
IEEE DOI 2210
Face recognition, Object detection, Detectors, Benchmark testing, Libraries, 3D from multi-view and sensors, retrieval BibRef

Wei, Y.[Yi], Wei, Z.[Zibu], Rao, Y.M.[Yong-Ming], Li, J.X.[Jia-Xin], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection,
ECCV22(XXIX:179-195).
Springer DOI 2211
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Xu, X.W.[Xiu-Wei], Wang, Y.F.[Yi-Fan], Zheng, Y.[Yu], Rao, Y.M.[Yong-Ming], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Back to Reality: Weakly-supervised 3D Object Detection with Shape-Guided Label Enhancement,
CVPR22(8428-8437)
IEEE DOI 2210
Training, Annotations, Shape, Layout, Object detection, Detectors, 3D from multi-view and sensors, Recognition: detection, retrieval BibRef

Wang, Z.Y.[Zi-Yi], Rao, Y.M.[Yong-Ming], Yu, X.[Xumin], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation,
CVPR22(11809-11819)
IEEE DOI 2210
Point cloud compression, Image segmentation, Fuses, Semantics, Transforms, Transformers, Segmentation, 3D from multi-view and sensors BibRef

Rao, Y.M.[Yong-Ming], Liu, B.[Benlin], Wei, Y.[Yi], Lu, J.W.[Ji-Wen], Hsieh, C.J.[Cho-Jui], Zhou, J.[Jie],
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection,
ICCV21(3263-3272)
IEEE DOI 2203
Solid modeling, Semantics, Layout, Training data, Object detection, Benchmark testing, Detection and localization in 2D and 3D, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zheng, Y.[Yu], Zhang, D.Y.[Dan-Yang], Xie, S.[Sinan], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Rotation-Robust Intersection over Union for 3d Object Detection,
ECCV20(XX:464-480).
Springer DOI 2011
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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
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Du, L.[Liang], Ye, X.Q.[Xiao-Qing], Tan, X.[Xiao], Johns, E.[Edward], Chen, B.[Bo], Ding, E.[Errui], Xue, X.Y.[Xiang-Yang], Feng, J.F.[Jian-Feng],
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network,
PAMI(44), No. 11, November 2022, pp. 8097-8109.
IEEE DOI 2210
Feature extraction, Object detection, Proposals, Transfer learning, Task analysis, Brain modeling, 3D object detection, autonomous driving BibRef

Zhang, Q.J.[Qi-Jian], Hou, J.H.[Jun-Hui], Qian, Y.[Yue], Chan, A.B.[Antoni B.], Zhang, J.Y.[Ju-Yong], He, Y.[Ying],
RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds,
IJCV(130), No. 12, December 2022, pp. 3100-3122.
Springer DOI 2211

WWW Link. BibRef

Tian, B.[Beiwen], Luo, L.[Liyi], Zhao, H.[Hao], Zhou, G.[Guyue],
VIBUS: Data-efficient 3D scene parsing with VIewpoint Bottleneck and Uncertainty-Spectrum modeling,
PandRS(194), 2022, pp. 302-318.
Elsevier DOI 2212
3D scene understanding, Self-supervised learning, Weakly-supervised representation learning, Spectral clustering BibRef

Luo, X.Z.[Xi-Zhao], Zhou, F.[Feng], Tao, C.B.[Chong-Ben], Yang, A.[Anjia], Zhang, P.[Peiyun], Chen, Y.H.[Yong-Hua],
Dynamic Multitarget Detection Algorithm of Voxel Point Cloud Fusion Based on PointRCNN,
ITS(23), No. 11, November 2022, pp. 20707-20720.
IEEE DOI 2212
Feature extraction, Point cloud compression, Object detection, Cameras, Heuristic algorithms, Autonomous vehicles, multi-feature fusion BibRef

Liu, A.A.[An-An], Guo, F.B.[Fu-Bin], Zhou, H.Y.[He-Yu], Yan, C.G.[Cheng-Gang], Gao, Z.[Zan], Li, X.Y.[Xuan-Ya], Li, W.H.[Wen-Hui],
Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval,
Cyber(52), No. 12, December 2022, pp. 13862-13873.
IEEE DOI 2212
Feature extraction, Mutual information, Protocols, 3-D object retrieval, cross-domain retrieval, multiview BibRef

Zoumpekas, T.[Thanasis], Salamó, M.[Maria], Puig, A.[Anna],
Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Luo, Z.W.[Zi-Wei], Xie, Z.[Zhong], Wan, J.[Jie], Zeng, Z.Y.[Zi-Yin], Liu, L.[Lu], Tao, L.[Liufeng],
Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Xiao, K.[Kai], Qian, J.[Jia], Li, T.[Teng], Peng, Y.X.[Yuan-Xi],
Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Yin, L.M.[Ling-Mei], Tian, W.[Wei], Wang, L.[Ling], Wang, Z.[Zhiang], Yu, Z.P.[Zhuo-Ping],
SPV-SSD: An Anchor-Free 3D Single-Stage Detector with Supervised-Point Rendering and Visibility Representation,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Gao, Y.B.[Yong-Bin], Liu, X.B.[Xue-Bing], Li, J.[Jun], Fang, Z.J.[Zhi-Jun], Jiang, X.Y.[Xiao-Yan], Huq, K.M.S.[Kazi Mohammed Saidul],
LFT-Net: Local Feature Transformer Network for Point Clouds Analysis,
ITS(24), No. 2, February 2023, pp. 2158-2168.
IEEE DOI 2302
Point cloud compression, Transformers, Task analysis, Feature extraction, Convolution, Semantics, 6G, point cloud, segmentation BibRef

Chen, H.[Hui], Xie, T.T.[Ting-Ting], Liang, M.[Man], Liu, W.Q.[Wan-Quan], Liu, P.X.P.[Peter Xiao-Ping],
A local tangent plane distance-based approach to 3D point cloud segmentation via clustering,
PR(137), 2023, pp. 109307.
Elsevier DOI 2302
3D point cloud, Plane segmentation, Tangent distance, Adaptive clustering BibRef

Liu, K.C.[Kang-Cheng],
RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging,
IJCV(131), No. 1, January 2023, pp. 938-967.
Springer DOI 2303
BibRef

Ning, K.L.[Kang-Lin], Liu, Y.F.[Yan-Fei], Su, Y.Z.[Yan-Zhao], Jiang, K.[Ke],
Point-Voxel and Bird-Eye-View Representation Aggregation Network for Single Stage 3D Object Detection,
ITS(24), No. 3, March 2023, pp. 3223-3235.
IEEE DOI 2303
Feature extraction, Detectors, Point cloud compression, Convolution, Transformers, Semantics, Point cloud, vision transformer BibRef

Pop, A.[Alexandru], Dom?a, V.[Victor], Tamas, L.[Levente],
Rotation Invariant Graph Neural Network for 3D Point Clouds,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
Rotation Normalization. Then matching. BibRef

Zhang, W.J.[Wen Jing], Su, S.Z.[Song Zhi], Hong, Q.Q.[Qing Qi], Wang, B.Z.[Bei Zhan], Sun, L.[Li],
Long short-distance topology modelling of 3D point cloud segmentation with a graph convolution neural network,
IET-CV(17), No. 3, 2023, pp. 251-264.
DOI Link 2305
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Ye, S.[Shuquan], Chen, D.D.[Dong-Dong], Han, S.[Songfang], Liao, J.[Jing],
Robust Point Cloud Segmentation With Noisy Annotations,
PAMI(45), No. 6, June 2023, pp. 7696-7710.
IEEE DOI 2305
BibRef
Earlier:
Learning with Noisy Labels for Robust Point Cloud Segmentation,
ICCV21(6423-6432)
IEEE DOI 2203
Noise measurement, Point cloud compression, Task analysis, Image segmentation, Annotations, Image edge detection, Point cloud, noisy label. Correlation, Upper bound, Lead, Robustness, Optimization and learning methods, 3D from multiview and other sensors BibRef

Wang, Q.[Qiang], Li, Z.Y.[Zi-Yu], Zhu, D.J.[De-Jun], Yang, W.K.[Wan-Kou],
LiDAR-only 3D object detection based on spatial context,
JVCIR(93), 2023, pp. 103805.
Elsevier DOI 2305
3D object detection, Convolutional neural network, LiDAR, Deep learning BibRef

Shi, G.S.[Guang-Sheng], Wang, K.[Ke], Li, R.F.[Rui-Feng], Ma, C.[Chao],
Real-Time Point Cloud Object Detection via Voxel-Point Geometry Abstraction,
ITS(24), No. 6, June 2023, pp. 5971-5982.
IEEE DOI 2306
Proposals, Point cloud compression, Feature extraction, Object detection, Representation learning, Geometry, point clouds BibRef

Tan, T.[Thon], Lim, J.M.Y.[Joanne Mun-Yee], Foo, J.J.[Ji Jinn], Muniandy, R.[Ramachandran],
3D detection transformer: Set prediction of objects using point clouds,
CVIU(236), 2023, pp. 103808.
Elsevier DOI 2310
Deep learning, 3D object detection, Point clouds, Transformers, Single-stage detector BibRef

Liu, Y.F.[Yi-Fan], Li, W.Y.[Wu-Yang], Liu, J.[Jie], Chen, H.[Hui], Yuan, Y.X.[Yi-Xuan],
GRAB-Net: Graph-Based Boundary-Aware Network for Medical Point Cloud Segmentation,
MedImg(42), No. 9, September 2023, pp. 2776-2786.
IEEE DOI 2310
BibRef

Tan, X.[Xin], Ma, Q.H.[Qi-Hang], Gong, J.Y.[Jing-Yu], Xu, J.C.[Jia-Chen], Zhang, Z.Z.[Zhi-Zhong], Song, H.C.[Hai-Chuan], Qu, Y.Y.[Yan-Yun], Xie, Y.[Yuan], Ma, L.Z.[Li-Zhuang],
Positive-Negative Receptive Field Reasoning for Omni-Supervised 3D Segmentation,
PAMI(45), No. 12, December 2023, pp. 15328-15344.
IEEE DOI 2311
BibRef
Earlier: A3, A4, A1, A6, A7, A8, A9, Only:
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

Yang, Y.[Yiran], Sun, X.[Xian], Diao, W.H.[Wen-Hui], Rong, X.[Xuee], Yan, S.[Shiyao], Yin, D.[Dongshuo], Li, X.M.[Xin-Ming],
Optimal Partition Assignment for Universal Object Detection,
MultMed(25), 2023, pp. 7582-7593.
IEEE DOI 2311
BibRef

He, X.[Xuan], Wang, Z.[Zian], Lin, J.C.[Jia-Cheng], Nai, K.[Ke], Yuan, J.[Jin], Li, Z.Y.[Zhi-Yong],
DO-SA&R: Distant Object Augmented Set Abstraction and Regression for Point-Based 3D Object Detection,
IP(32), 2023, pp. 5852-5864.
IEEE DOI Code:
WWW Link. 2311
BibRef

Zhu, Y.J.[Yi-Jie], Xie, J.M.[Jing-Ming], Liu, M.[Moyun], Yao, L.[Lei], Chen, Y.[Youping],
BF3D: Bi-directional fusion 3D detector with semantic sampling and geometric mapping,
IVC(139), 2023, pp. 104835.
Elsevier DOI Code:
WWW Link. 2311
Deep learning, 3D object detection, Bi-directional fusion, Semantic sampling, Geometric mapping BibRef

Ning, Y.Q.[Ya-Qian], Cao, J.[Jie], Bao, C.[Chun], Hao, Q.[Qun],
DVST: Deformable Voxel Set Transformer for 3D Object Detection from Point Clouds,
RS(15), No. 23, 2023, pp. 5612.
DOI Link 2312
BibRef

Zhu, G.[Guanyu], Zhou, Y.[Yong], Yao, R.[Rui], Zhu, H.C.[Han-Cheng],
Cross-Class Bias Rectification for Point Cloud Few-Shot Segmentation,
MultMed(25), 2023, pp. 9175-9188.
IEEE DOI 2312
BibRef

Su, Y.Y.[Yong-Yi], Xu, X.[Xun], Jia, K.[Kui],
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning,
CirSysVideo(33), No. 12, December 2023, pp. 7723-7736.
IEEE DOI 2312
BibRef

Wen, S.[Shuhuan], Li, P.J.[Peng-Jiang], Zhang, H.[Hong],
Hybrid Cross-Transformer-KPConv for Point Cloud Segmentation,
SPLetters(31), 2024, pp. 126-130.
IEEE DOI 2401
BibRef

Xiao, K.[Kai], Li, T.[Teng], Li, J.[Jun], Huang, D.[Da], Peng, Y.Y.X.[Yuan-Yan-Xi],
Equal Emphasis on Data and Network: A Two-Stage 3D Point Cloud Object Detection Algorithm with Feature Alignment,
RS(16), No. 2, 2024, pp. 249.
DOI Link 2402
BibRef

Feng, Y.F.[Yi-Fan], Ji, S.Y.[Shu-Yi], Liu, Y.S.[Yu-Shen], Du, S.[Shaoyi], Dai, Q.H.[Qiong-Hai], Gao, Y.[Yue],
Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval,
PAMI(46), No. 4, April 2024, pp. 2206-2223.
IEEE DOI 2403
Task analysis, Solid modeling, Point cloud compression, Correlation, Data models, Feature extraction, Hypergraph, memory bank BibRef


Fan, L.[Lue], Yang, Y.[Yuxue], Mao, Y.M.[Yi-Ming], Wang, F.[Feng], Chen, Y.T.[Yun-Tao], Wang, N.[Naiyan], Zhang, Z.X.[Zhao-Xiang],
Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection,
ICCV23(19763-19772)
IEEE DOI Code:
WWW Link. 2401
BibRef

Tang, P.[Pin], Xu, H.M.[Hai-Ming], Ma, C.[Chao],
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation,
ICCV23(3314-3324)
IEEE DOI 2401
BibRef

Wu, G.[Guile], Cao, T.T.[Tong-Tong], Liu, B.B.[Bing-Bing], Chen, X.X.[Xing-Xin], Ren, Y.[Yuan],
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer,
ICCV23(8635-8644)
IEEE DOI 2401
BibRef

Onghena, P.[Pierre], Gigli, L.[Leonardo], Velasco-Forero, S.[Santiago],
Rotation-invariant Hierarchical Segmentation on Poincaré Ball for 3D Point Cloud,
SHARP23(1757-1766)
IEEE DOI 2401
BibRef

Hui, L.[Le], Tang, L.[Linghua], Dai, Y.C.[Yu-Chao], Xie, J.[Jin], Yang, J.[Jian],
Efficient LiDAR Point Cloud Oversegmentation Network,
ICCV23(17957-17966)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yang, C.K.[Cheng-Kun], Chen, M.H.[Min-Hung], Chuang, Y.Y.[Yung-Yu], Lin, Y.Y.[Yen-Yu],
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision,
ICCV23(977-987)
IEEE DOI Code:
WWW Link. 2401
BibRef

Peri, N.[Neehar], Li, M.[Mengtian], Wilson, B.[Benjamin], Wang, Y.X.[Yu-Xiong], Hays, J.[James], Ramanan, D.[Deva],
An Empirical Analysis of Range for 3D Object Detection,
BRAVO23(4076-4085)
IEEE DOI 2401
BibRef

Kong, L.[Lingdong], Liu, Y.[Youquan], Chen, R.[Runnan], Ma, Y.X.[Yue-Xin], Zhu, X.G.[Xin-Ge], Li, Y.[Yikang], Hou, Y.N.[Yue-Nan], Qiao, Y.[Yu], Liu, Z.W.[Zi-Wei],
Rethinking Range View Representation for LiDAR Segmentation,
ICCV23(228-240)
IEEE DOI 2401
BibRef

Liu, Y.Q.[You-Quan], Chen, R.[Runnan], Li, X.[Xin], Kong, L.[Lingdong], Yang, Y.C.[Yu-Chen], Xia, Z.Y.[Zhao-Yang], Bai, Y.Q.[Ye-Qi], Zhu, X.G.[Xin-Ge], Ma, Y.X.[Yue-Xin], Li, Y.[Yikang], Qiao, Y.[Yu], Hou, Y.N.[Yue-Nan],
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase,
ICCV23(21605-21616)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lu, Y.H.[Yu-Hang], Jiang, Q.[Qi], Chen, R.[Runnan], Hou, Y.N.[Yue-Nan], Zhu, X.G.[Xin-Ge], Ma, Y.X.[Yue-Xin],
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data,
ICCV23(21617-21627)
IEEE DOI 2401
BibRef

Reichardt, L.[Laurenz], Ebert, N.[Nikolas], Wasenmüller, O.[Oliver],
360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation,
LIMIT23(1067-1075)
IEEE DOI 2401
BibRef

Li, P.[Pufan], Gao, X.[Xiang], Hu, Q.J.[Qian-Jiang], Hu, W.[Wei],
Robust Graph-Based Segmentation of Noisy Point Clouds,
ICIP23(3090-3094)
IEEE DOI 2312
BibRef

Cheng, Z.Y.[Zhong-Yao], Chen, C.[Cen], Zhao, Z.Y.[Zi-Yuan], Qian, P.S.[Pei-Sheng], Li, X.L.[Xiao-Li], Yang, X.[Xulei],
COCO-TEACH: A Contrastive Co-Teaching Network For Incremental 3D Object Detection,
ICIP23(1990-1994)
IEEE DOI 2312
BibRef

Decatur, D.[Dale], Lang, I.[Itai], Hanocka, R.[Rana],
3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions,
CVPR23(20930-20939)
IEEE DOI 2309
BibRef

Chen, Y.H.[Yu-Hao], Gunraj, H.[Hayden], Zeng, E.Z.[E. Zhixuan], Meyer, R.[Robbie], Gilles, M.[Maximilian], Wong, A.[Alexander],
MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy,
FaDE-TCV23(68-77)
IEEE DOI 2309
BibRef

Schön, R.[Robin], Ludwig, K.[Katja], Lienhart, R.[Rainer],
Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance,
L3D-IVU23(4809-4819)
IEEE DOI 2309
BibRef

Wang, Y.J.[Ying-Jie], Deng, J.J.[Jia-Jun], Li, Y.[Yao], Hu, J.[Jinshui], Liu, C.[Cong], Zhang, Y.[Yu], Ji, J.M.[Jian-Min], Ouyang, W.L.[Wan-Li], Zhang, Y.[Yanyong],
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection,
CVPR23(13394-13403)
IEEE DOI 2309
BibRef

Zhang, N.[Nan], Pan, Z.[Zhiyi], Li, T.H.[Thomas H.], Gao, W.[Wei], Li, G.[Ge],
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering,
CVPR23(1244-1254)
IEEE DOI 2309
BibRef

Li, G.R.[Guang-Rui], Kang, G.L.[Guo-Liang], Wang, X.H.[Xiao-Han], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Adversarially Masking Synthetic to Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation,
CVPR23(20464-20474)
IEEE DOI 2309
BibRef

Chen, A.[Anthony], Zhang, K.[Kevin], Zhang, R.[Renrui], Wang, Z.[Zihan], Lu, Y.H.[Yu-Heng], Guo, Y.D.[Yan-Dong], Zhang, S.H.[Shang-Hang],
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection,
CVPR23(5291-5301)
IEEE DOI 2309
BibRef

Lu, Y.H.[Yu-Heng], Xu, C.F.[Chen-Feng], Wei, X.B.[Xiao-Bao], Xie, X.D.[Xiao-Dong], Tomizuka, M.[Masayoshi], Keutzer, K.[Kurt], Zhang, S.H.[Shang-Hang],
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation,
CVPR23(1190-1199)
IEEE DOI 2309
BibRef

Zhu, Z.[Ziyue], Meng, Q.[Qiang], Wang, X.[Xiao], Wang, K.[Ke], Yan, L.J.[Liu-Jiang], Yang, J.[Jian],
Curricular Object Manipulation in LiDAR-Based Object Detection,
CVPR23(1125-1135)
IEEE DOI 2309
BibRef

Lei, J.[Jiahui], Deng, C.Y.[Cong-Yue], Schmeckpeper, K.[Karl], Guibas, L.J.[Leonidas J.], Daniilidis, K.[Kostas],
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision,
CVPR23(4902-4912)
IEEE DOI 2309
BibRef

Rajendran, V.[Vickram], Tang, C.[Chuck], van Paasschen, F.[Frits],
Improving Rare Classes on nuScenes LiDAR segmentation Through Targeted Domain Adaptation,
WAD23(130-139)
IEEE DOI 2309
BibRef

Malic, D.[Dušan], Fruhwirth-Reisinger, C.[Christian], Possegger, H.[Horst], Bischof, H.[Horst],
SAILOR: Scaling Anchors via Insights into Latent Object Representation,
WACV23(623-632)
IEEE DOI 2302
Training, Adaptation models, Solid modeling, Laser radar, Object detection, Detectors, Algorithms: 3D computer vision BibRef

Luo, Z.P.[Zhi-Peng], Zhang, G.[Gongjie], Zhou, C.Q.[Chang-Qing], Liu, T.R.[Tian-Rui], Lu, S.J.[Shi-Jian], Pan, L.[Liang],
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection,
WACV23(4219-4228)
IEEE DOI 2302
Point cloud compression, Location awareness, Fuses, Object detection, Benchmark testing, Algorithms: 3D computer vision BibRef

Erabati, G.K.[Gopi Krishna], Araujo, H.[Helder],
Li3DeTr: A LiDAR based 3D Detection Transformer,
WACV23(4239-4248)
IEEE DOI 2302
Point cloud compression, Knowledge engineering, Laser radar, Convolution, Object detection BibRef

Qian, X.[Xuelin], Wang, L.[Li], Zhu, Y.[Yi], Zhang, L.[Li], Fu, Y.W.[Yan-Wei], Xue, X.Y.[Xiang-Yang],
ImpDet: Exploring Implicit Fields for 3D Object Detection,
WACV23(4249-4259)
IEEE DOI 2302
Location awareness, Representation learning, Point cloud compression, Semantics, Object detection, segmentation BibRef

Wu, C.Z.[Cheng-Zhi], Bi, X.[Xuelei], Pfrommer, J.[Julius], Cebulla, A.[Alexander], Mangold, S.[Simon], Beyerer, J.[Jürgen],
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly,
WACV23(4520-4529)
IEEE DOI 2302
Point cloud compression, Learning systems, Service robots, Transfer learning, Pipelines, Data models, Applications: Robotics BibRef

Lee, D.[Daeun], Kim, J.[Jinkyu],
Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling,
WACV23(682-691)
IEEE DOI 2302
Weight measurement, Training, Roads, Semantics, Detectors, Solids, Algorithms: Image recognition and understanding (object detection, Robotics BibRef

Loiseau, R.[Romain], Aubry, M.[Mathieu], Landrieu, L.[Loďc],
Online Segmentation of LiDAR Sequences: Dataset and Algorithm,
ECCV22(XXXVIII:301-317).
Springer DOI 2211
BibRef

Sharma, G.[Gopal], Yin, K.X.[Kang-Xue], Maji, S.[Subhransu], Kalogerakis, E.[Evangelos], Litany, O.[Or], Fidler, S.[Sanja],
MvDeCor: Multi-view Dense Correspondence Learning for Fine-Grained 3D Segmentation,
ECCV22(II:550-567).
Springer DOI 2211
BibRef

Yang, H.H.[Hong-Hui], Liu, Z.L.[Zi-Li], Wu, X.P.[Xiao-Pei], Wang, W.X.[Wen-Xiao], Qian, W.[Wei], He, X.F.[Xiao-Fei], Cai, D.[Deng],
Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph,
ECCV22(VIII:662-679).
Springer DOI 2211
BibRef

Yang, P.[Pei], Wang, H.[Huan],
Regional Saliency Map Attack for Medical Image Segmentation,
ICIP22(846-850)
IEEE DOI 2211
Training, Measurement, Image segmentation, Visualization, Perturbation methods, Semantics, Neural networks, Index Terms, medical image segmentation BibRef

Wu, Z.H.[Zhong-Hua], Wu, Y.C.[Yi-Cheng], Lin, G.S.[Guo-Sheng], Cai, J.F.[Jian-Fei], Qian, C.[Chen],
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation,
ECCV22(XXXI:78-96).
Springer DOI 2211
BibRef

Ye, M.S.[Mao-Sheng], Wan, R.[Rui], Xu, S.J.[Shuang-Jie], Cao, T.[Tongyi], Chen, Q.F.[Qi-Feng],
Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks,
ECCV22(XXIX:196-212).
Springer DOI 2211
BibRef

Saltori, C.[Cristiano], Galasso, F.[Fabio], Fiameni, G.[Giuseppe], Sebe, N.[Nicu], Poiesi, F.[Fabio], Ricci, E.[Elisa],
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation,
PAMI(45), No. 12, December 2023, pp. 14234-14247.
IEEE DOI 2311
BibRef
Earlier: A1, A2 A3, A4, A6, A5: Saltori, C.[Cristiano], Galasso, F.[Fabio],
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation,
ECCV22(XXXIII:586-602).
Springer DOI 2211
BibRef

Saltori, C.[Cristiano], Krivosheev, E.[Evgeny], Lathuiliére, S.[Stéphane], Sebe, N.[Nicu], Galasso, F.[Fabio], Fiameni, G.[Giuseppe], Ricci, E.[Elisa], Poiesi, F.[Fabio],
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation,
ECCV22(XXXIII:567-585).
Springer DOI 2211
BibRef

Doll, S.[Simon], Schulz, R.[Richard], Schneider, L.[Lukas], Benzin, V.[Viviane], Enzweiler, M.[Markus], Lensch, H.P.A.[Hendrik P. A.],
SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images With Global Cross-Sensor Attention,
ECCV22(XXIX:230-245).
Springer DOI 2211
BibRef

Liu, C.[Chang], Qian, X.Y.[Xiao-Yan], Huang, B.X.[Bin-Xiao], Qi, X.J.[Xiao-Juan], Lam, E.[Edmund], Tan, S.C.[Siew-Chong], Wong, N.[Ngai],
Multimodal Transformer for Automatic 3D Annotation and Object Detection,
ECCV22(XXXVIII:657-673).
Springer DOI 2211
BibRef

Zhou, Z.X.[Zi-Xiang], Zhao, X.C.[Xiang-Chen], Wang, Y.[Yu], Wang, P.[Panqu], Foroosh, H.[Hassan],
CenterFormer: Center-Based Transformer for 3D Object Detection,
ECCV22(XXXVIII:496-513).
Springer DOI 2211
BibRef

Hwang, J.J.[Jyh-Jing], Kretzschmar, H.[Henrik], Manela, J.[Joshua], Rafferty, S.[Sean], Armstrong-Crews, N.[Nicholas], Chen, T.[Tiffany], Anguelov, D.[Dragomir],
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection,
ECCV22(XXXVIII:388-405).
Springer DOI 2211
BibRef

Yin, J.[Junbo], Zhou, D.F.[Ding-Fu], Zhang, L.J.[Liang-Jun], Fang, J.[Jin], Xu, C.Z.[Cheng-Zhong], Shen, J.B.[Jian-Bing], Wang, W.G.[Wen-Guan],
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection,
ECCV22(XXIX:17-33).
Springer DOI 2211
BibRef

Carranza-García, M.[Manuel], Riquelme, J.C.[José C.], Zakhor, A.[Avideh],
Temporal Axial Attention For Lidar-Based 3d Object Detection In Autonomous Driving,
ICIP22(201-205)
IEEE DOI 2211
Laser radar, Pipelines, Object detection, Streaming media, Feature extraction, autonomous driving, attention, deep learning, object detection BibRef

Chen, K.[Keng], Zhou, F.[Feng], Dai, J.[Ju], Shen, P.[Pei], Cai, X.Q.[Xing-Quan], Zhang, F.Q.[Feng-Quan],
MCGNet: Multi-Level Context-aware and Geometric-aware Network for 3D Object Detection,
ICIP22(1846-1850)
IEEE DOI 2211
Point cloud compression, Image edge detection, Object detection, Performance gain, Feature extraction, Proposals, 3D Point Clouds, 3D Bounding Boxes BibRef

Williams, F.[Francis], Gojcic, Z.[Zan], Khamis, S.[Sameh], Zorin, D.[Denis], Bruna, J.[Joan], Fidler, S.[Sanja], Litany, O.[Or],
Neural Fields as Learnable Kernels for 3D Reconstruction,
CVPR22(18479-18489)
IEEE DOI 2210

WWW Link. Training, Linear systems, Codes, Shape, Computational modeling, Vision+graphics BibRef

Shi, H.Y.[Han-Yu], Wei, J.C.[Jia-Cheng], Li, R.B.[Rui-Bo], Liu, F.[Fayao], Lin, G.S.[Guo-Sheng],
Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation,
CVPR22(11830-11839)
IEEE DOI 2210
Point cloud compression, Training, Solid modeling, Annotations, Computational modeling, Segmentation, Self- semi- meta- Video analysis and understanding BibRef

Yang, C.K.[Cheng-Kun], Wu, J.J.[Ji-Jia], Chen, K.S.[Kai-Syun], Chuang, Y.Y.[Yung-Yu], Lin, Y.Y.[Yen-Yu],
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation,
CVPR22(11820-11829)
IEEE DOI 2210
Point cloud compression, Adaptation models, Image segmentation, Image recognition, Semantics, Transformers, Segmentation, Efficient learning and inferences BibRef

Zhang, C.[Cheng], Wan, H.C.[Hao-Cheng], Shen, X.[Xinyi], Wu, Z.Z.[Zi-Zhao],
PatchFormer: An Efficient Point Transformer with Patch Attention,
CVPR22(11789-11798)
IEEE DOI 2210
Point cloud compression, Shape, Computational modeling, Transformers, Pattern recognition, Vision+graphics BibRef

Wang, X.L.[Xin-Long], Yu, Z.D.[Zhi-Ding], de Mello, S.[Shalini], Kautz, J.[Jan], Anandkumar, A.[Anima], Shen, C.H.[Chun-Hua], Alvarez, J.M.[Jose M.],
FreeSOLO: Learning to Segment Objects without Annotations,
CVPR22(14156-14166)
IEEE DOI 2210
Location awareness, Image segmentation, Image recognition, Annotations, Manuals, Pattern recognition, Recognition: detection, Self- semi- meta- unsupervised learning BibRef

Liu, L.[Leyao], Zheng, T.[Tian], Lin, Y.J.[Yun-Jou], Ni, K.[Kai], Fang, L.[Lu],
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation,
CVPR22(18953-18962)
IEEE DOI 2210
Performance evaluation, Uncertainty, Convolution, Shape, Semantics, Pipelines, grouping and shape analysis, retrieval, Segmentation, Recognition: detection BibRef

Uy, M.A.[Mikaela Angelina], Chang, Y.Y.[Yen-Yu], Sung, M.[Minhyuk], Goel, P.[Purvi], Lambourne, J.[Joseph], Birdal, T.[Tolga], Guibas, L.J.[Leonidas J.],
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders,
CVPR22(11840-11850)
IEEE DOI 2210
Point cloud compression, Training, Solid modeling, Visualization, Shape, Reverse engineering, Segmentation, Vision + graphics BibRef

Zheng, W.[Wu], Hong, M.X.[Ming-Xuan], Jiang, L.[Li], Fu, C.W.[Chi-Wing],
Boosting 3D Object Detection by Simulating Multimodality on Point Clouds,
CVPR22(13628-13637)
IEEE DOI 2210
Measurement, Training, Laser radar, Semantics, Detectors, Filling, Recognition: detection, categorization, retrieval, Scene analysis and understanding BibRef

Nie, D.[Dong], Lan, R.[Rui], Wang, L.[Ling], Ren, X.F.[Xiao-Feng],
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation,
CVPR22(17263-17273)
IEEE DOI 2210
Point cloud compression, Representation learning, Image segmentation, Fuses, Semantics, grouping and shape analysis BibRef

Deng, S.H.[Sheng-Heng], Liang, Z.H.[Zhi-Hao], Sun, L.[Lin], Jia, K.[Kui],
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention,
CVPR22(8438-8447)
IEEE DOI 2210
Point cloud compression, Laser radar, Fuses, Benchmark testing, Pattern recognition, Proposals, 3D from multi-view and sensors, retrieval BibRef

Fan, L.[Lue], Pang, Z.Q.[Zi-Qi], Zhang, T.Y.[Tian-Yuan], Wang, Y.X.[Yu-Xiong], Zhao, H.[Hang], Wang, F.[Feng], Wang, N.[Naiyan], Zhang, Z.X.[Zhao-Xiang],
Embracing Single Stride 3D Object Detector with Sparse Transformer,
CVPR22(8448-8458)
IEEE DOI 2210
Point cloud compression, Navigation, Detectors, Object detection, Sensor phenomena and characterization, Transformers, Navigation and autonomous driving BibRef

Zhong, J.X.[Jia-Xing], Zhou, K.[Kaichen], Hu, Q.Y.[Qing-Yong], Wang, B.[Bing], Trigoni, N.[Niki], Markham, A.[Andrew],
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces,
CVPR22(8500-8510)
IEEE DOI 2210
Point cloud compression, Pain, Tracking, Computational modeling, Dynamics, Surgery, 3D from multi-view and sensors, Video analysis and understanding BibRef

Xue, Y.J.[Yu-Jing], Mao, J.G.[Jia-Geng], Niu, M.Z.[Min-Zhe], Xu, H.[Hang], Mi, M.B.[Michael Bi], Zhang, W.[Wei], Wang, X.G.[Xiao-Gang], Wang, X.C.[Xin-Chao],
Point2Seq: Detecting 3D Objects as Sequences,
CVPR22(8511-8520)
IEEE DOI 2210
Training, Solid modeling, Robot vision systems, Object detection, Predictive models, Decoding, 3D from multi-view and sensors, Robot vision BibRef

Liu, C.D.[Chuan-Dong], Gao, C.Q.[Chen-Qiang], Liu, F.[Fangcen], Liu, J.[Jiang], Meng, D.Y.[De-Yu], Gao, X.B.[Xin-Bo],
SS3D: Sparsely-Supervised 3D Object Detection from Point Cloud,
CVPR22(8418-8427)
IEEE DOI 2210
Training, Point cloud compression, Annotations, Filtering, Detectors, Object detection, 3D from multi-view and sensors, Robot vision BibRef

Tang, L.[Liyao], Zhan, Y.B.[Yi-Bing], Chen, Z.[Zhe], Yu, B.S.[Bao-Sheng], Tao, D.C.[Da-Cheng],
Contrastive Boundary Learning for Point Cloud Segmentation,
CVPR22(8479-8489)
IEEE DOI 2210
Point cloud compression, Measurement, Codes, Computational modeling, Pattern recognition, Scene analysis and understanding BibRef

Lai, X.[Xin], Liu, J.H.[Jian-Hui], Jiang, L.[Li], Wang, L.W.[Li-Wei], Zhao, H.S.[Heng-Shuang], Liu, S.[Shu], Qi, X.J.[Xiao-Juan], Jia, J.Y.[Jia-Ya],
Stratified Transformer for 3D Point Cloud Segmentation,
CVPR22(8490-8499)
IEEE DOI 2210
Point cloud compression, Transformers, Encoding, Computational efficiency, Pattern recognition, grouping and shape analysis BibRef

Hu, J.S.K.[Jordan S.K.], Kuai, T.S.[Tian-Shu], Waslander, S.L.[Steven L.],
Point Density-Aware Voxels for LiDAR 3D Object Detection,
CVPR22(8459-8468)
IEEE DOI 2210
Point cloud compression, Laser radar, Navigation, Object detection, Feature extraction, Navigation and autonomous driving BibRef

You, Y.R.[Yu-Rong], Luo, K.[Katie], Phoo, C.P.[Cheng Perng], Chao, W.L.[Wei-Lun], Sun, W.[Wen], Hariharan, B.[Bharath], Campbell, M.[Mark], Weinberger, K.Q.[Kilian Q.],
Learning to Detect Mobile Objects from LiDAR Scans Without Labels,
CVPR22(1120-1130)
IEEE DOI 2210
Training, Laser radar, Navigation, Detectors, Pattern recognition, Sensors, Recognition: detection, categorization, retrieval, Transfer/low-shot/long-tail learning BibRef

Schinagl, D.[David], Krispel, G.[Georg], Possegger, H.[Horst], Roth, P.M.[Peter M.], Bischof, H.[Horst],
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data,
CVPR22(1131-1140)
IEEE DOI 2210
Point cloud compression, Laser radar, Detectors, Object detection, Recognition: detection, Robot vision 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, 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, Feature extraction, Stereo, Transfer/Low-shot/Semi/Unsupervised Learning 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.S.[Yu-Shi], 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.J.[Shuang-Jie], Cao, T.Y.[Tong-Yi], 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.H.[Jun-Hao], 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.B.[Wen-Bo], 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

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, 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

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

Li, Y.W.[Ying-Wei], Qi, C.R.[Charles R.], Zhou, Y.[Yin], Liu, C.X.[Chen-Xi], Anguelov, D.[Dragomir],
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences,
CVPR23(9329-9339)
IEEE DOI 2309
BibRef

Qi, C.R.[Charles R.], Zhou, Y.[Yin], Najibi, M.[Mahyar], Sun, P.[Pei], Vo, K.[Khoa], Deng, B.Y.[Bo-Yang], 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.F.[Ji-Feng], 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.M.[Xiao-Meng], 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.W.[Yu-Wen], 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

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 .


Last update:Mar 16, 2024 at 20:36:19