Yang, B.S.[Bi-Sheng],
Dong, Z.[Zhen],
A shape-based segmentation method for mobile laser scanning point
clouds,
PandRS(81), No. 1, July 2013, pp. 19-30.
Elsevier DOI
1306
Point classification; Object segmentation; Mobile laser
scanning; Object extraction
BibRef
Yang, B.S.[Bi-Sheng],
Li, Y.H.[Yu-Hao],
Zou, X.H.[Xiang-Hong],
Dong, Z.[Zhen],
A Marker-free Calibration Method for Mobile Laser Scanning Point Clouds
Correction,
ISPRS20(B2:347-354).
DOI Link
2012
BibRef
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
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
Li, X.O.[Xia-Ohan],
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
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, 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
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
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
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
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
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
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
Wang, Z.Y.[Zi-Yi],
Rao, Y.M.[Yong-Ming],
Yu, X.[Xumin],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Point-to-Pixel Prompting for Point Cloud Analysis With Pre-Trained
Image Models,
PAMI(46), No. 6, June 2024, pp. 4381-4397.
IEEE DOI
2405
Point cloud compression, Task analysis, Solid modeling, Tuning,
Analytical models, Feature extraction, Distillation, point cloud, prompt tuning
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
BibRef
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
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
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
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
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
BibRef
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
BibRef
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
BibRef
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
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
BibRef
Ye, S.Q.[Shu-Quan],
Chen, D.D.[Dong-Dong],
Han, S.F.[Song-Fang],
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
Ye, S.Q.[Shu-Quan],
Xu, Y.[Yan],
Chen, D.D.[Dong-Dong],
Han, S.F.[Song-Fang],
Liao, J.[Jing],
Learning a Single Network for Robust Medical Image Segmentation With
Noisy Labels,
MedImg(43), No. 9, September 2024, pp. 3188-3199.
IEEE DOI
2409
Noise measurement, Image edge detection, Noise, Image segmentation,
Biomedical imaging, Reliability, Annotations,
label denoising
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
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
Zhu, G.[Guanyu],
Zhou, Y.[Yong],
Yao, R.[Rui],
Zhu, H.C.[Han-Cheng],
Information Gap Narrowing for Point Cloud Few-Shot Segmentation,
CirSysVideo(34), No. 6, June 2024, pp. 4421-4433.
IEEE DOI
2406
Point cloud compression, Prototypes, Task analysis,
Feature extraction, Data models, Prediction algorithms,
co-occurrent objects
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
Zhang, X.Y.[Xin-Yuan],
Han, F.[Fei],
Shen, S.[Shiyang],
Wang, Y.C.[Yi-Cheng],
Xu, S.[Shilong],
Dong, X.[Xiao],
Hu, Y.H.[Yi-Hua],
Target region extraction and segmentation algorithm for reflective
tomography Lidar image,
IET-IPR(17), No. 4, 2023, pp. 1001-1009.
DOI Link
2303
image segmentation, Lidar, reflective tomography, target region extraction
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, A.[Aoran],
Guan, D.[Dayan],
Zhang, X.Q.[Xiao-Qin],
Lu, S.J.[Shi-Jian],
Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial
Consistency,
MultMed(26), 2024, pp. 5536-5547.
IEEE DOI
2404
Point cloud compression, Laser radar, Training, Adaptation models,
Perturbation methods, Task analysis, LiDAR point clouds, deep learning
BibRef
Xiao, A.[Aoran],
Huang, J.X.[Jia-Xing],
Liu, K.C.[Kang-Cheng],
Guan, D.[Dayan],
Zhang, X.Q.[Xiao-Qin],
Lu, S.J.[Shi-Jian],
Domain Adaptive LiDAR Point Cloud Segmentation via Density-Aware
Self-Training,
ITS(25), No. 10, October 2024, pp. 13627-13639.
IEEE DOI
2410
Point cloud compression, Laser radar, Labeling, Adaptation models,
Training, Semantic segmentation, LiDAR point cloud, deep learning
BibRef
Jiang, Y.[Yun],
Liu, B.X.[Bing-Xi],
Zhang, Z.Q.[Ze-Qun],
Yan, Y.[Yao],
Guo, H.[Huanting],
Li, Y.H.[Yu-Hang],
Dense-sparse representation matters: A point-based method for
volumetric medical image segmentation,
JVCIR(100), 2024, pp. 104115.
Elsevier DOI
2405
BibRef
And:
Corrigendum:
JVCIR(102), 2024, pp. 104205.
Elsevier DOI
2407
Convolutional neural networks, Transformer, Volumetric images, Point cloud
BibRef
Umam, A.[Ardian],
Yang, C.K.[Cheng-Kun],
Chuang, J.H.[Jen-Hui],
Lin, Y.Y.[Yen-Yu],
Unsupervised Point Cloud Co-Part Segmentation via Co-Attended
Superpoint Generation and Aggregation,
MultMed(26), 2024, pp. 7775-7786.
IEEE DOI
2405
Point cloud compression, Semantics, Shape, Image segmentation,
Task analysis, Annotations, Point cloud segmentation,
unsupervised learning
BibRef
Zhu, Q.F.[Qin-Feng],
Fan, L.[Lei],
Weng, N.X.[Ning-Xin],
Advancements in point cloud data augmentation for deep learning:
A survey,
PR(153), 2024, pp. 110532.
Elsevier DOI
2405
Point cloud, Augmentation, Deep learning, Detection,
Segmentation, Classification
BibRef
Wu, Z.H.[Zhong-Hua],
Wu, Y.C.[Yi-Cheng],
Lin, G.S.[Guo-Sheng],
Cai, J.F.[Jian-Fei],
Reliability-Adaptive Consistency Regularization for Weakly-Supervised
Point Cloud Segmentation,
IJCV(132), No. 6, June 2024, pp. 2276-2289.
Springer DOI
2406
BibRef
Wei, J.C.[Jia-Cheng],
Lin, G.S.[Guo-Sheng],
Yap, K.H.[Kim-Hui],
Liu, F.[Fayao],
Hung, T.Y.[Tzu-Yi],
Dense Supervision Propagation for Weakly Supervised Semantic
Segmentation on 3D Point Clouds,
CirSysVideo(34), No. 6, June 2024, pp. 4367-4377.
IEEE DOI
2406
Point cloud compression, Training, Semantic segmentation, Labeling,
Decoding, Convolution, 3D point cloud, weakly supervised learning,
semantic 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
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:
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
Xie, T.T.[Ting-Ting],
Chen, H.[Hui],
Liu, W.Q.[Wan-Quan],
Zhou, R.Y.[Rong-Yu],
Li, Q.[Qilin],
3D surface segmentation from point clouds via quadric fits based on
DBSCAN clustering,
PR(154), 2024, pp. 110589.
Elsevier DOI
2406
3D point clouds, Surface extraction, DBSCAN, Quadric surface
BibRef
Liu, B.[Bo],
Zeng, H.[Hui],
Dong, Q.[Qiulei],
Hu, Z.Y.[Zhan-Yi],
Language-Level Semantics-Conditioned 3D Point Cloud Segmentation,
RS(16), No. 13, 2024, pp. 2376.
DOI Link
2407
BibRef
Kumar, P.[Prashant],
Makwana, D.[Dhruv],
Susladkar, O.[Onkar],
Mittal, A.[Anurag],
Kalra, P.K.[Prem Kumar],
MOVES: Movable and moving LiDAR scene segmentation in label-free
settings using static reconstruction,
PR(155), 2024, pp. 110651.
Elsevier DOI
2408
Label-free LiDAR segmentation, Static LiDAR reconstruction, Dynamic environments
BibRef
Wang, X.[Xuchu],
Yuan, Y.[Yue],
MNAT-Net: Multi-Scale Neighborhood Aggregation Transformer Network
for Point Cloud Classification and Segmentation,
ITS(25), No. 8, August 2024, pp. 9153-9167.
IEEE DOI
2408
Point cloud compression, Transformers, Feature extraction,
Task analysis, Natural language processing, Convolution, transformer
BibRef
Xie, K.[Kai],
Zhu, J.Z.[Jian-Zhong],
Ren, H.[He],
Wang, Y.H.[Ying-Hua],
Yang, W.[Wanneng],
Chen, G.[Gang],
Lin, C.[Chengda],
Zhai, R.[Ruifang],
Delving into the Potential of Deep Learning Algorithms for Point
Cloud Segmentation at Organ Level in Plant Phenotyping,
RS(16), No. 17, 2024, pp. 3290.
DOI Link
2409
BibRef
Song, Z.Y.[Zi-Yang],
Yang, B.[Bo],
Unsupervised 3D Object Segmentation of Point Clouds by Geometry
Consistency,
PAMI(46), No. 12, December 2024, pp. 8459-8473.
IEEE DOI
2411
Point cloud compression, Object segmentation,
Motion segmentation, Annotations, Geometry, Vectors,
unsupervised learning
BibRef
Du, Z.J.[Zi-Jin],
Liang, J.Q.[Jian-Qing],
Liang, J.[Jiye],
Yao, K.X.[Kai-Xuan],
Cao, F.L.[Fei-Long],
Graph Regulation Network for Point Cloud Segmentation,
PAMI(46), No. 12, December 2024, pp. 7940-7955.
IEEE DOI
2411
Point cloud compression, Feature extraction, Prototypes, Convolution,
Regulation, Transformers, Graph, heterophily, homophily, segmentation
BibRef
Zhuang, X.W.[Xian-Wei],
Wang, H.[Hualiang],
He, X.X.[Xiao-Xuan],
Fu, S.[Siming],
Hu, H.J.[Hao-Ji],
SemiGMMPoint: Semi-supervised point cloud segmentation based on
Gaussian mixture models,
PR(158), 2025, pp. 111045.
Elsevier DOI Code:
WWW Link.
2411
3D point cloud segmentation, Semi-supervised learning,
Generative classifier, Gaussian mixture models, Contrastive learning
BibRef
Lin, J.X.[Jing-Xin],
Zhong, K.F.[Kai-Fan],
Gong, T.[Tao],
Zhang, X.M.[Xian-Min],
Wang, N.F.[Nian-Feng],
Point Cloud Segmentation Neural Network with Same-Type Point Cloud
Assistance,
IVC(152), 2024, pp. 105331.
Elsevier DOI
2412
Neural network, Same-type point cloud, Point cloud segmentation
BibRef
Chen, M.[Meida],
Han, K.[Kangle],
Yu, Z.[Zifan],
Feng, A.[Andrew],
Hou, Y.[Yu],
You, S.[Suya],
Soibelman, L.[Lucio],
An Aerial Photogrammetry Benchmark Dataset for Point Cloud
Segmentation and Style Translation,
RS(16), No. 22, 2024, pp. 4240.
DOI Link
2412
BibRef
Shu, Z.Y.[Zhen-Yu],
Li, S.[Shiyang],
Xin, S.Q.[Shi-Qing],
Liu, L.G.[Li-Gang],
3D Shape Segmentation With Potential Consistency Mining and
Enhancement,
MultMed(27), 2025, pp. 133-144.
IEEE DOI
2501
Shape, Feature extraction, Faces, Vectors, Point cloud compression,
Partitioning algorithms, Labeling, Data mining, Accuracy, shape analysis
BibRef
Xu, R.J.[Rui-Jie],
Zhang, C.[Chuyu],
Ren, H.[Hui],
He, X.M.[Xu-Ming],
Dual-level Adaptive Self-labeling for Novel Class Discovery in Point
Cloud Segmentation,
ECCV24(XIII: 288-305).
Springer DOI
2412
BibRef
Zou, T.[Tianpei],
Qu, S.Q.[San-Qing],
Li, Z.J.[Zhi-Jun],
Knoll, A.[Alois],
He, L.[Lianghua],
Chen, G.[Guang],
Jiang, C.J.[Chang-Jun],
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3d
Point Cloud Segmentation,
ECCV24(LV: 19-36).
Springer DOI
2412
BibRef
Peng, X.[Xidong],
Chen, R.[Runnan],
Qiao, F.[Feng],
Kong, L.D.[Ling-Dong],
Liu, Y.[Youquan],
Sun, Y.J.[Yu-Jing],
Wang, T.[Tai],
Zhu, X.G.[Xin-Ge],
Ma, Y.X.[Yue-Xin],
Learning to Adapt Sam for Segmenting Cross-domain Point Clouds,
ECCV24(XLIII: 54-71).
Springer DOI
2412
BibRef
Zhang, Z.Y.[Zhi-Yuan],
Yang, L.C.[Li-Cheng],
Xiang, Z.Y.[Zhi-Yu],
Risurconv: Rotation Invariant Surface Attention-augmented Convolutions
for 3D Point Cloud Classification and Segmentation,
ECCV24(XXVIII: 93-109).
Springer DOI
2412
BibRef
Li, L.[Li],
Shum, H.P.H.[Hubert P. H.],
Breckon, T.P.[Toby P.],
Rapid-SEG: Range-aware Pointwise Distance Distribution Networks for 3d
Lidar Segmentation,
ECCV24(VII: 222-241).
Springer DOI
2412
BibRef
Yamaguchi, M.[Masahiro],
Higa, K.[Kyota],
Hosoi, T.[Toshinori],
Shibata, T.[Takashi],
Robust 3D Semantic Segmentation with Incomplete Point Clouds Based on
Sequential Frame Sampling,
ICIP24(3526-3532)
IEEE DOI
2411
Point cloud compression, Semantic segmentation, Employment,
Semantics, Indoor environment, Safety, 3D Semantic Segmentation,
Incomplete Point Clouds
BibRef
Stearns, C.[Colton],
Fu, A.[Alex],
Liu, J.[Jiateng],
Park, J.J.[Jeong Joon],
Rempe, D.[Davis],
Paschalidou, D.[Despoina],
Guibas, L.J.[Leonidas J.],
CurveCloudNet: Processing Point Clouds with 1D Structure,
CVPR24(27981-27991)
IEEE DOI
2410
Point cloud compression, Laser radar, Accuracy, Cognition, Sensors,
point cloud, 3D perception, LiDAR, 3D segmentation, autonomous vehicles
BibRef
Janda, A.[Andrej],
Wagstaff, B.[Brandon],
Ng, E.G.[Edwin G.],
Kelly, J.[Jonathan],
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud
Segmentation Networks With Image Data,
CRV23(145-152)
IEEE DOI
2406
Point cloud compression, Training, Visualization, Solid modeling,
Annotations, Semantic segmentation, self supervised, pre training,
point cloud segmentation
BibRef
Cho, W.W.[Won-Woo],
Choi, D.[Dongmin],
Lim, H.[Hyesu],
Choi, J.H.[Jin-Ho],
Choi, S.[Saemee],
Min, H.S.[Hyun-Seok],
Lim, S.[Sungbin],
Choo, J.[Jaegul],
Slice and Conquer: A Planar-to-3D Framework for Efficient Interactive
Segmentation of Volumetric Images,
WACV24(7599-7608)
IEEE DOI
2404
Image segmentation, Shape, Pipelines, Human in the loop,
Space exploration, Applications, Image recognition and understanding
BibRef
Sun, H.[Haowen],
Duan, Y.[Yueqi],
Yan, J.C.[Jun-Cheng],
Liu, Y.F.[Yi-Fan],
Lu, J.W.[Ji-Wen],
MirageRoom: 3D Scene Segmentation with 2D Pre-Trained Models by
Mirage Projection,
CVPR24(20237-20246)
IEEE DOI
2410
Point cloud compression, Solid modeling, Image segmentation,
Cameras, 3D point cloud, mirage projection
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
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
Kong, L.D.[Ling-Dong],
Liu, Y.Q.[You-Quan],
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
Chen, Y.H.[Yu-Hao],
Gunraj, H.[Hayden],
Zeng, E. .Z.X.[E. Zhi-Xuan],
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
Zhang, N.[Nan],
Pan, Z.Y.[Zhi-Yi],
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
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
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
Loiseau, R.[Romain],
Aubry, M.[Mathieu],
Landrieu, L.[Loïc],
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, 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
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
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
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, 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
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
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, 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, grouping and shape analysis
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
Lê, 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
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
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
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
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
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
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
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
Zheng, T.,
Chen, C.,
Yuan, J.,
Li, B.,
Ren, K.,
PointCloud Saliency Maps,
ICCV19(1598-1606)
IEEE DOI
2004
Code, Saliency.
WWW Link. convolutional neural nets, image classification,
image representation, image segmentation, object detection, DGCNN,
Predictive models
BibRef
Zhang, Z.Y.[Zhi-Yuan],
Hua, B.S.[Binh-Son],
Yeung, S.K.[Sai-Kit],
ShellNet: Efficient Point Cloud Convolutional Neural Networks Using
Concentric Shells Statistics,
ICCV19(1607-1616)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, image representation, image segmentation,
Semantics
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
Range and Color, RGB-D Segmentation and Analysis .