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Elsevier DOI
1306
Airborne LiDAR; Filtering; Progressive TIN densification;
Point cloud segmentation; Segmentation using smoothness constraint
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1505
Segmentation
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3D point cloud over-segmentation, 3D point cloud segmentation,
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Tao, W.Y.[Wu-Yong],
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and
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2011
Machine learning, Feature extraction,
Convolution, Laser modes, Shape, Fitting, 3-D point cloud,
segmentation
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IEEE DOI
1806
Tensile stress, Frequency estimation,
Estimation, Covariance matrices, Laplace equations, Hypergraph,
spectral clustering
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Relation Graph Network for 3D Object Detection in Point Clouds,
IP(30), 2021, pp. 92-107.
IEEE DOI
2011
Proposals, Object detection, Feature extraction, Laser radar,
deep learning
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Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds,
PAMI(43), No. 10, October 2021, pp. 3664-3680.
IEEE DOI
2109
BibRef
Earlier:
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical
Kernel,
CVPR20(11608-11617)
IEEE DOI
2008
BibRef
Earlier:
Octree Guided CNN With Spherical Kernels for 3D Point Clouds,
CVPR19(9623-9632).
IEEE DOI
2002
Kernel, Convolution, Neural networks,
Feature extraction, Semantics, Computer architecture, semantic segmentation.
Convolutional codes, Integrated circuits, Robustness
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Hsu, P.H.[Pai-Hui],
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Incorporating Handcrafted Features into Deep Learning for Point Cloud
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DOI Link
2011
BibRef
Li, X.[Xiaohan],
Chen, L.[Lu],
Li, S.[Shuang],
Zhou, X.[Xiang],
Depth segmentation in real-world scenes based on U-V disparity
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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.],
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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
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Guarda, A.F.R.[André F. R.],
Rodrigues, N.M.M.[Nuno M. M.],
Pereira, F.[Fernando],
Neighborhood Adaptive Loss Function for Deep Learning-Based Point
Cloud Coding With Implicit and Explicit Quantization,
MultMedMag(28), No. 3, July 2021, pp. 107-116.
IEEE DOI
2109
Encoding, Deep learning, Distortion, Geometry, Machine learning,
Image coding, Point cloud coding, explicit quantization
BibRef
Tian, Y.F.[Yi-Fei],
Chen, L.[Long],
Song, W.[Wei],
Sung, Y.S.[Yun-Sick],
Woo, S.C.[Sang-Chul],
DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object
Recognition in Point Cloud,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Su, F.[Fei],
Zhu, H.H.[Hai-Hong],
Chen, T.Y.[Tao-Yi],
Li, L.[Lin],
Yang, F.[Fan],
Peng, H.X.[Hui-Xiang],
Tang, L.[Lei],
Zuo, X.K.[Xin-Kai],
Liang, Y.F.[Yi-Fan],
Ying, S.[Shen],
An anchor-based graph method for detecting and classifying indoor
objects from cluttered 3D point clouds,
PandRS(172), 2021, pp. 114-131.
Elsevier DOI
2101
Point cloud, Object classification, Functional part,
Graph matching, Super-graph, Graph similarity
BibRef
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Point cloud classification with deep normalized Reeb graph
convolution,
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Elsevier DOI
2102
Reeb graph, Point cloud, Graph normalization
BibRef
Ma, L.,
Li, Y.,
Li, J.,
Tan, W.,
Yu, Y.,
Chapman, M.A.,
Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object
Segmentation From LiDAR Point Clouds in Large-Scale Environments,
ITS(22), No. 2, February 2021, pp. 821-836.
IEEE DOI
2102
Feature extraction, Semantics, Shape,
Solid modeling, Neural networks, Roads, Point clouds, k-nearest neighbor
BibRef
Geng, X.X.[Xiao-Xiao],
Ji, S.P.[Shun-Ping],
Lu, M.[Meng],
Zhao, L.L.[Ling-Li],
Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Luo, N.[Nan],
Yu, H.Q.[Hong-Quan],
Huo, Z.F.[Zhen-Feng],
Liu, J.H.[Jin-Hui],
Wang, Q.[Quan],
Xu, Y.[Ying],
Gao, Y.[Yun],
KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network
for Point Cloud Segmentation,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Li, G.Y.[Gong-Yang],
Liu, Z.[Zhi],
Chen, M.Y.[Min-Yu],
Bai, Z.[Zhen],
Lin, W.S.[Wei-Si],
Ling, H.B.[Hai-Bin],
Hierarchical Alternate Interaction Network for RGB-D Salient Object
Detection,
IP(30), 2021, pp. 3528-3542.
IEEE DOI
2103
Object detection, Feature extraction, Color, Visualization,
Task analysis, Computer architecture, Stereo image processing,
alternate interaction
BibRef
Liu, Z.Y.[Zheng-Yi],
Wang, Y.[Yuan],
Tan, Y.C.[Ya-Cheng],
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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],
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ECCV20(XVII:665-681).
Springer DOI
2011
BibRef
Zhang, X. .L.[Xin- Liang],
Fu, C.L.[Chen-Lin],
Zhao, Y.J.[Yun-Ji],
Xu, X.Z.[Xiao-Zhuo],
Hybrid feature CNN model for point cloud classification and
segmentation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4086-4091.
DOI Link
2103
BibRef
Wang, Q.[Qi],
Chen, J.[Jian],
Deng, J.Q.[Jian-Qiang],
Zhang, X.F.[Xin-Fang],
3D-CenterNet: 3D object detection network for point clouds with
center estimation priority,
PR(115), 2021, pp. 107884.
Elsevier DOI
2104
3D object detection, Point cloud, Deep learning
BibRef
Li, D.W.[Da-Wei],
Shi, G.L.[Guo-Liang],
Wu, Y.H.[Yu-Hao],
Yang, Y.P.[Yan-Ping],
Zhao, M.B.[Ming-Bo],
Multi-Scale Neighborhood Feature Extraction and Aggregation for Point
Cloud Segmentation,
CirSysVideo(31), No. 6, June 2021, pp. 2175-2191.
IEEE DOI
2106
Feature extraction, Semantics,
Image segmentation, Data mining,
point cloud segmentation
BibRef
Chen, C.F.[Chao-Fan],
Qian, S.S.[Sheng-Sheng],
Fang, Q.[Quan],
Xu, C.S.[Chang-Sheng],
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud
Segmentation,
MultMed(23), 2021, pp. 2335-2346.
IEEE DOI
2108
Feature extraction, Task analysis,
Layout, Logic gates, Machine learning,
gated graph attention network
BibRef
Zhu, J.F.[Jian-Feng],
Sui, L.C.[Li-Chun],
Zang, Y.[Yufu],
Zheng, H.[He],
Jiang, W.[Wei],
Zhong, M.[Mianqing],
Ma, F.[Fei],
Classification of Airborne Laser Scanning Point Cloud Using
Point-Based Convolutional Neural Network,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Brown, K.[Kyle],
Bourbakis, N.[Nikolaos],
Curve and Surface Fitting Techniques in Computer Vision,
IJIG(21), No. 4, October 2021 2021, pp. 2150041.
DOI Link
2110
BibRef
Guo, Y.L.[Yu-Lan],
Wang, H.Y.[Han-Yun],
Hu, Q.Y.[Qing-Yong],
Liu, H.[Hao],
Liu, L.[Li],
Bennamoun, M.[Mohammed],
Deep Learning for 3D Point Clouds: A Survey,
PAMI(43), No. 12, December 2021, pp. 4338-4364.
IEEE DOI
2112
Solid modeling, Deep learning,
Object detection, Laser radar, Task analysis, Sensors, Deep learning,
part segmentation
BibRef
Yang, F.[Fei],
Davoine, F.[Franck],
Wang, H.[Huan],
Jin, Z.[Zhong],
Continuous conditional random field convolution for point cloud
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PR(122), 2022, pp. 108357.
Elsevier DOI
2112
Point cloud segmentation, Conditional random fields,
Message passing, Graph convolution, Mean-field approximation
BibRef
Zhang, J.[Jing],
Wang, J.J.[Jia-Jun],
Xu, D.[Da],
Li, Y.S.[Yun-Song],
HCNET: A Point Cloud Object Detection Network Based on Height and
Channel Attention,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Huang, H.[Hao],
Li, X.[Xiang],
Wang, L.J.[Ling-Jing],
Fang, Y.[Yi],
3D-MetaConNet: Meta-learning for 3D Shape Classification and
Segmentation,
3DV21(982-991)
IEEE DOI
2201
Training, Representation learning, Adaptation models,
Solid modeling, Shape, Computational modeling
BibRef
Li, X.Y.[Xing-Ye],
Zhang, L.[Ling],
Zhu, Z.G.[Zhi-Gang],
SnapshotNet: Self-supervised feature learning for point cloud data
segmentation using minimal labeled data,
CVIU(216), 2022, pp. 103339.
Elsevier DOI
2202
Self-supervision, Point cloud, Semantic segmentation
BibRef
Jing, W.P.[Wei-Peng],
Zhang, W.J.[Wen-Jun],
Li, L.[Linhui],
Di, D.L.[Dong-Lin],
Chen, G.S.[Guang-Sheng],
Wang, J.[Jian],
AGNet: An Attention-Based Graph Network for Point Cloud
Classification and Segmentation,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Wang, F.[Fei],
Zhuang, Y.[Yan],
Zhang, H.[Hong],
Gu, H.[Hong],
Real-Time 3-D Semantic Scene Parsing With LiDAR Sensors,
Cyber(52), No. 3, March 2022, pp. 1351-1363.
IEEE DOI
2203
Convolution, Semantics, Real-time systems, Laser radar,
Tensile stress, Task analysis, 3-D convolutional neural network,
sparse (ST)
BibRef
Qiu, S.[Shi],
Anwar, S.[Saeed],
Barnes, N.[Nick],
Geometric Back-Projection Network for Point Cloud Classification,
MultMed(24), No. 2022, pp. 1943-1955.
IEEE DOI
2204
BibRef
Earlier:
Dense-Resolution Network for Point Cloud Classification and
Segmentation,
WACV21(3812-3821)
IEEE DOI
2106
Feature extraction, Task analysis, Geometry, Visualization, Shape,
Redundancy, Point Cloud Classification, 3D Deep Learning,
Error-correcting Feedback.
Training, Visualization, Adaptation models,
Computational modeling
BibRef
Li, Z.[Ziyu],
Yao, Y.[Yuncong],
Quan, Z.B.[Zhi-Bin],
Xie, J.[Jin],
Yang, W.K.[Wan-Kou],
Spatial information enhancement network for 3D object detection from
point cloud,
PR(128), 2022, pp. 108684.
Elsevier DOI
2205
3D object detection, Autonomous vehicles, Point cloud,
LiDAR sensor, 3D shape completion
BibRef
Wang, M.M.[Ming-Ming],
Chen, Q.[Qingkui],
Fu, Z.B.[Zhi-Bing],
LSNet: Learned Sampling Network for 3D Object Detection from Point
Clouds,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Zhao, C.[Chen],
Yang, J.Q.[Jia-Qi],
Xiong, X.[Xin],
Zhu, A.F.[Ang-Fan],
Cao, Z.G.[Zhi-Guo],
Li, X.[Xin],
Rotation invariant point cloud analysis:
Where local geometry meets global topology,
PR(127), 2022, pp. 108626.
Elsevier DOI
2205
Point cloud analysis, Rotation invariance, Deep learning,
Classification, Segmentation
BibRef
Xu, B.[Bo],
Chen, Z.[Zhen],
Zhu, Q.[Qing],
Ge, X.M.[Xu-Ming],
Huang, S.Z.[Sheng-Zhi],
Zhang, Y.T.[Ye-Ting],
Liu, T.Y.[Tian-Yang],
Wu, D.[Di],
Geometrical Segmentation of Multi-Shape Point Clouds Based on
Adaptive Shape Prediction and Hybrid Voting RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Wang, B.X.[Bing-Xu],
Lan, J.H.[Jin-Hui],
Gao, J.[Jiangjiang],
LiDAR Filtering in 3D Object Detection Based on Improved RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Cetinkaya, B.[Bedrettin],
Kalkan, S.[Sinan],
Akbas, E.[Emre],
Does depth estimation help object detection?,
IVC(122), 2022, pp. 104427.
Elsevier DOI
2205
Object detection, Depth estimation, RGB-D
BibRef
Elich, C.[Cathrin],
Oswald, M.R.[Martin R.],
Pollefeys, M.[Marc],
Stueckler, J.[Joerg],
Weakly supervised learning of multi-object 3D scene decompositions
using deep shape priors,
CVIU(220), 2022, pp. 103440.
Elsevier DOI
2206
Multi-object 3D scene representation learning
BibRef
Song, Z.J.[Zhan-Jie],
Zhao, L.Q.[Lin-Qing],
Zhou, J.[Jie],
Learning Hybrid Semantic Affinity for Point Cloud Segmentation,
CirSysVideo(32), No. 7, July 2022, pp. 4599-4612.
IEEE DOI
2207
Semantics, Point cloud compression, Image segmentation,
Solid modeling, Task analysis, Learning systems,
graph convolutional network
BibRef
Chen, Y.[Ye],
Liu, J.X.[Jin-Xian],
Ni, B.B.[Bing-Bing],
Wang, H.[Hang],
Yang, J.C.[Jian-Cheng],
Liu, N.[Ning],
Li, T.[Teng],
Tian, Q.[Qi],
Shape Self-Correction for Unsupervised Point Cloud Understanding,
ICCV21(8362-8371)
IEEE DOI
2203
Point cloud compression, Deep learning, Analytical models, Shape,
Pipelines, Feature extraction,
Representation learning
BibRef
Nie, X.[Xing],
Liu, Y.C.[Yong-Cheng],
Chen, S.H.[Shao-Hong],
Chang, J.L.[Jian-Long],
Huo, C.L.[Chun-Lei],
Meng, G.F.[Gao-Feng],
Tian, Q.[Qi],
Hu, W.M.[Wei-Ming],
Pan, C.H.[Chun-Hong],
Differentiable Convolution Search for Point Cloud Processing,
ICCV21(7417-7426)
IEEE DOI
2203
To enable CNN.
Point cloud compression, Directed acyclic graph, Convolution,
Shape, Heuristic algorithms, Computer architecture, Segmentation,
Representation learning
BibRef
Lę, E.T.[Eric-Tuan],
Sung, M.[Minhyuk],
Ceylan, D.[Duygu],
Mech, R.[Radomir],
Boubekeur, T.[Tamy],
Mitra, N.J.[Niloy J.],
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point
Clouds,
ICCV21(7438-7446)
IEEE DOI
2203
Point cloud compression, Codes, Fitting, Merging,
Reverse engineering, Pipelines, Segmentation, grouping and shape,
BibRef
Yang, J.Y.[Ju-Young],
Ahn, P.[Pyunghwan],
Kim, D.Y.[Do-Yeon],
Lee, H.[Haeil],
Kim, J.[Junmo],
Progressive Seed Generation Auto-Encoder for Unsupervised Point Cloud
Learning,
ICCV21(6393-6402)
IEEE DOI
2203
Point cloud compression, Annotations, Focusing,
Computer architecture, Feature extraction, Stereo,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Ye, S.[Shuquan],
Chen, D.D.[Dong-Dong],
Han, S.[Songfang],
Liao, J.[Jing],
Learning with Noisy Labels for Robust Point Cloud Segmentation,
ICCV21(6423-6432)
IEEE DOI
2203
Point cloud compression, Image segmentation, Correlation,
Upper bound, Lead, Robustness, Optimization and learning methods,
3D from multiview and other sensors
BibRef
Venkatesh, R.[Rahul],
Karmali, T.[Tejan],
Sharma, S.[Sarthak],
Ghosh, A.[Aurobrata],
Babu, R.V.[R. Venkatesh],
Jeni, L.A.[Lászlo A.],
Singh, M.[Maneesh],
Deep Implicit Surface Point Prediction Networks,
ICCV21(12633-12642)
IEEE DOI
2203
Point cloud compression, Solid modeling, Shape,
Computational modeling, Predictive models,
3D from multiview and other sensors
BibRef
Hui, L.[Le],
Yuan, J.[Jia],
Cheng, M.[Mingmei],
Xie, J.[Jin],
Zhang, X.Y.[Xiao-Ya],
Yang, J.[Jian],
Superpoint Network for Point Cloud Oversegmentation,
ICCV21(5490-5499)
IEEE DOI
2203
Point cloud compression, Codes, Semantics, Stereo,
3D from multiview and other sensors,
BibRef
Yan, S.M.[Si-Ming],
Yang, Z.P.[Zhen-Pei],
Ma, C.Y.[Chong-Yang],
Huang, H.B.[Hai-Bin],
Vouga, E.[Etienne],
Huang, Q.X.[Qi-Xing],
HPNet: Deep Primitive Segmentation Using Hybrid Representations,
ICCV21(2733-2742)
IEEE DOI
2203
Point cloud compression, Shape, Semantics, Performance gain,
Benchmark testing, Detection and localization in 2D and 3D,
grouping and shape
BibRef
Zou, L.[Longkun],
Tang, H.[Hui],
Chen, K.[Ke],
Jia, K.[Kui],
Geometry-Aware Self-Training for Unsupervised Domain Adaptation on
Object Point Clouds,
ICCV21(6383-6392)
IEEE DOI
2203
Point cloud compression, Geometry, Representation learning,
Location awareness, Shape, Semantics, Stereo,
BibRef
Mao, J.[Jiageng],
Niu, M.Z.[Min-Zhe],
Bai, H.Y.[Hao-Yue],
Liang, X.D.[Xiao-Dan],
Xu, H.[Hang],
Xu, C.J.[Chun-Jing],
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D
Object Detection,
ICCV21(2703-2712)
IEEE DOI
2203
Point cloud compression, Solid modeling, Adaptation models,
Focusing, Object detection,
Vision for robotics and autonomous vehicles
BibRef
Xu, J.Y.[Jian-Yun],
Zhang, R.X.[Rui-Xiang],
Dou, J.[Jian],
Zhu, Y.[Yushi],
Sun, J.[Jie],
Pu, S.L.[Shi-Liang],
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for
LiDAR Point Cloud Segmentation,
ICCV21(16004-16013)
IEEE DOI
2203
Point cloud compression, Quantization (signal), Laser radar,
Image resolution, Logic gates, Solids,
3D from multiview and other sensors
BibRef
Yang, C.K.[Cheng-Kun],
Chuang, Y.Y.[Yung-Yu],
Lin, Y.Y.[Yen-Yu],
Unsupervised Point Cloud Object Co-segmentation by Co-contrastive
Learning and Mutual Attention Sampling,
ICCV21(7315-7324)
IEEE DOI
2203
Point cloud compression, Deep learning, Correlation, Annotations,
Neural networks, Segmentation, grouping and shape,
3D from multiview and other sensors
BibRef
Ye, M.S.[Mao-Sheng],
Xu, S.[Shuangjie],
Cao, T.[Tongyi],
Chen, Q.F.[Qi-Feng],
DRINet: A Dual-Representation Iterative Learning Network for Point
Cloud Segmentation,
ICCV21(7427-7436)
IEEE DOI
2203
Point cloud compression, Representation learning, Degradation,
Runtime, Costs, Feature extraction, Segmentation, grouping and shape,
Vision for robotics and autonomous vehicles
BibRef
Wei, Y.M.[Yi-Min],
Liu, H.[Hao],
Xie, T.T.[Ting-Ting],
Ke, Q.H.[Qiu-Hong],
Guo, Y.L.[Yu-Lan],
Spatial-Temporal Transformer for 3D Point Cloud Sequences,
WACV22(657-666)
IEEE DOI
2202
Point cloud compression, Solid modeling,
Aggregates, Semantics, Grouping and Shape
BibRef
Shakibajahromi, B.[Bahareh],
Shayestehmanesh, S.[Saeed],
Schwartz, D.[Daniel],
Shokoufandeh, A.[Ali],
HyNet: 3D Segmentation Using Hybrid Graph Networks,
3DV21(805-814)
IEEE DOI
2201
Representation learning, Deep learning, Solid modeling, Shape,
Biological system modeling, Focusing
BibRef
Cen, J.[Jun],
Yun, P.[Peng],
Cai, J.[Junhao],
Wang, M.Y.[Michael Yu],
Liu, M.[Ming],
Open-set 3D Object Detection,
3DV21(869-878)
IEEE DOI
2201
Measurement, Point cloud compression, Upper bound, Laser radar,
Object detection, Open systems
BibRef
Brodeur, T.[Tristan],
Ali Akbarpour, H.[Hadi],
Suddarth, S.[Steve],
Point Cloud Object Segmentation Using Multi Elevation-Layer 2D
Bounding-Boxes,
WAAMI21(3910-3918)
IEEE DOI
2112
Measurement, Octrees, Merging, Object segmentation
BibRef
Hu, W.[Wenbo],
Zhao, H.S.[Heng-Shuang],
Jiang, L.[Li],
Jia, J.Y.[Jia-Ya],
Wong, T.T.[Tien-Tsin],
Bidirectional Projection Network for Cross Dimension Scene
Understanding,
CVPR21(14368-14377)
IEEE DOI
2111
Geometry, Visualization,
Semantics, Benchmark testing, Image representation
BibRef
Huang, C.[Chao],
Cao, Z.J.[Zhang-Jie],
Wang, Y.[Yunbo],
Wang, J.M.[Jian-Min],
Long, M.S.[Ming-Sheng],
MetaSets: Meta-Learning on Point Sets for Generalizable
Representations,
CVPR21(8859-8868)
IEEE DOI
2111
Geometry, Training, Deep learning, Solid modeling,
Benchmark testing
BibRef
Yi, L.[Li],
Gong, B.Q.[Bo-Qing],
Funkhouser, T.[Thomas],
Complete amp; Label: A Domain Adaptation Approach to Semantic
Segmentation of LiDAR Point Clouds,
CVPR21(15358-15368)
IEEE DOI
2111
Training, Laser radar, Semantics, Transforms, Manuals, Sensors
BibRef
Gong, J.Y.[Jing-Yu],
Xu, J.C.[Jia-Chen],
Tan, X.[Xin],
Song, H.C.[Hai-Chuan],
Qu, Y.Y.[Yan-Yun],
Xie, Y.[Yuan],
Ma, L.Z.[Li-Zhuang],
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field
Component Reasoning,
CVPR21(11668-11677)
IEEE DOI
2111
Codes, Semantics, Neural networks, Benchmark testing, Cognition, Entropy
BibRef
Lu, T.[Tao],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
CGA-Net: Category Guided Aggregation for Point Cloud Semantic
Segmentation,
CVPR21(11688-11697)
IEEE DOI
2111
Aggregates, Semantics, Computer architecture, Pattern recognition
BibRef
Fan, S.Q.[Si-Qi],
Dong, Q.L.[Qiu-Lei],
Zhu, F.[Fenghua],
Lv, Y.S.[Yi-Sheng],
Ye, P.J.[Pei-Jun],
Wang, F.Y.[Fei-Yue],
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point
Cloud Segmentation,
CVPR21(14499-14508)
IEEE DOI
2111
Semantics, Computer architecture, Network architecture, Pattern recognition
BibRef
Chen, H.W.[Hai-Wei],
Liu, S.C.[Shi-Chen],
Chen, W.K.[Wei-Kai],
Li, H.[Hao],
Hill, R.[Randall],
Equivariant Point Network for 3D Point Cloud Analysis,
CVPR21(14509-14518)
IEEE DOI
2111
Convolutional codes, Visualization,
Shape, Convolution, Computational modeling
BibRef
Zheng, W.[Wu],
Tang, W.L.[Wei-Liang],
Jiang, L.[Li],
Fu, C.W.[Chi-Wing],
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud,
CVPR21(14489-14498)
IEEE DOI
2111
Training, Matched filters,
Shape, Detectors, Object detection
BibRef
Peng, X.D.[Xi-Dong],
Zhu, X.G.[Xin-Ge],
Wang, T.[Tai],
Ma, Y.X.[Yue-Xin],
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance
Depth Estimation,
WACV22(225-234)
IEEE DOI
2202
Costs, Laser radar,
Computational modeling, Estimation, Detectors, Scene Understanding
BibRef
Zhu, X.G.[Xin-Ge],
Zhou, H.[Hui],
Wang, T.[Tai],
Hong, F.Z.[Fang-Zhou],
Ma, Y.X.[Yue-Xin],
Li, W.[Wei],
Li, H.S.[Hong-Sheng],
Lin, D.[Dahua],
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR
Segmentation,
CVPR21(9934-9943)
IEEE DOI
2111
Solid modeling, Laser radar,
Convolution, Network topology, Interference, Encoding
BibRef
Cheng, B.[Bowen],
Sheng, L.[Lu],
Shi, S.[Shaoshuai],
Yang, M.[Ming],
Xu, D.[Dong],
Back-tracing Representative Points for Voting-based 3D Object
Detection in Point Clouds,
CVPR21(8959-8968)
IEEE DOI
2111
Location awareness, Visualization,
Object detection, Feature extraction, Pattern recognition, Proposals
BibRef
Qi, C.R.[Charles R.],
Zhou, Y.[Yin],
Najibi, M.[Mahyar],
Sun, P.[Pei],
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Deng, B.[Boyang],
Anguelov, D.[Dragomir],
Offboard 3D Object Detection from Point Cloud Sequences,
CVPR21(6130-6140)
IEEE DOI
2111
Training, Pipelines, Detectors,
Object detection, Semisupervised learning, Real-time systems
BibRef
Tian, H.[Hao],
Chen, Y.T.[Yun-Tao],
Dai, J.[Jifeng],
Zhang, Z.X.[Zhao-Xiang],
Zhu, X.[Xizhou],
Unsupervised Object Detection with LiDAR Clues,
CVPR21(5958-5968)
IEEE DOI
2111
Training, Location awareness, Image segmentation,
Laser radar, Annotations, Object detection
BibRef
Li, Z.C.[Zhi-Chao],
Wang, F.[Feng],
Wang, N.[Naiyan],
LiDAR R-CNN: An Efficient and Universal 3D Object Detector,
CVPR21(7542-7551)
IEEE DOI
2111
Laser radar, Costs,
Codes, Detectors, Real-time systems
BibRef
Fang, J.[Jin],
Zuo, X.X.[Xin-Xin],
Zhou, D.[Dingfu],
Jin, S.Z.[Sheng-Ze],
Wang, S.[Sen],
Zhang, L.J.[Liang-Jun],
LiDAR-Aug: A General Rendering-based Augmentation Framework for 3D
Object Detection,
CVPR21(4708-4718)
IEEE DOI
2111
Training, Laser radar, Neural networks,
Training data, Object detection, Detectors
BibRef
Aguilar, C.[Camilo],
Comer, M.[Mary],
Hanhan, I.[Imad],
Agyei, R.[Ronald],
Sangid, M.[Michael],
3D Fiber Segmentation with Deep Center Regression and Geometric
Clustering,
CVMI21(3741-3749)
IEEE DOI
2109
WWW Link. Geometry, Training,
Image color analysis, Shape, Microscopy, Neural networks
BibRef
Xiao, C.X.[Chen-Xi],
Wachs, J.[Juan],
Triangle-Net: Towards Robustness in Point Cloud Learning,
WACV21(826-835)
IEEE DOI
2106
Service robots,
Surveillance, Neural networks, Feature extraction, Robustness
BibRef
Yang, Y.R.[Yi-Rong],
Fan, B.[Bin],
Liu, Y.C.[Yong-Cheng],
Lin, H.[Hua],
Zhang, J.Y.[Ji-Yong],
Liu, X.[Xin],
Cai, X.Y.[Xin-Yu],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Deep Space Probing for Point Cloud Analysis,
ICPR21(10235-10242)
IEEE DOI
2105
Geometry, Convolution, Neural networks,
Benchmark testing, Convolutional neural networks
BibRef
Lin, H.[Hua],
Fan, B.[Bin],
Liu, Y.C.[Yong-Cheng],
Yang, Y.R.[Yi-Rong],
Pan, Z.[Zheng],
Shi, J.B.[Jian-Bo],
Pan, C.H.[Chun-Hong],
Xie, H.W.[Hui-Wen],
PointSpherical: Deep Shape Context for Point Cloud Learning in
Spherical Coordinates,
ICPR21(10266-10273)
IEEE DOI
2105
Solid modeling, Shape, Convolution, Semantics, Feature extraction
BibRef
Alliegro, A.[Antonio],
Boscaini, D.[Davide],
Tommasi, T.[Tatiana],
Joint Supervised and Self-Supervised Learning for 3D Real World
Challenges,
ICPR21(6718-6725)
IEEE DOI
2105
Solid modeling, Shape,
Transfer learning, Supervised learning, Intelligent agents
BibRef
Pan, Y.[Yunyi],
Gan, Y.[Yuan],
Liu, K.[Kun],
Zhang, Y.[Yan],
Progressive Scene Segmentation Based on Self-Attention Mechanism,
ICPR21(3985-3992)
IEEE DOI
2105
Convolution, Semantics, Benchmark testing, Decoding, Task analysis,
3D Scene Understanding
BibRef
Zhong, M.[Min],
Zeng, G.[Gang],
Enhanced Vote Network for 3D Object Detection in Point Clouds,
ICPR21(6624-6631)
IEEE DOI
2105
Aggregates, Face recognition,
Semantics, Object detection, Benchmark testing, Feature extraction
BibRef
Demilew, S.S.[Selameab S.],
Aghdam, H.H.[Hamed H.],
Laganičre, R.[Robert],
Petriu, E.M.[Emil M.],
FA3D: Fast and Accurate 3d Object Detection,
ISVC20(I:397-409).
Springer DOI
2103
BibRef
Krishna, O.[Onkar],
Irie, G.[Go],
Wu, X.[Xiaomeng],
Kawanishi, T.[Takahito],
Kashino, K.[Kunio],
Adaptive Spotting: Deep Reinforcement Object Search in 3d Point Clouds,
ACCV20(III:257-272).
Springer DOI
2103
BibRef
Zhang, Y.[Yi],
Ye, Y.[Yuwen],
Xiang, Z.Y.[Zhi-Yu],
Gu, J.Q.[Jia-Qi],
Sdp-net: Scene Flow Based Real-time Object Detection and Prediction
from Sequential 3d Point Clouds,
ACCV20(I:140-157).
Springer DOI
2103
BibRef
Liu, X.,
Cao, J.,
Bi, Q.,
Wang, J.,
Shi, B.,
Wei, Y.,
Dense Point Diffusion for 3D Object Detection,
3DV20(762-770)
IEEE DOI
2102
Convolution, Object detection,
Feature extraction, Task analysis, Quantization (signal)
BibRef
Saltori, C.,
Lathuiličre, S.,
Sebe, N.,
Ricci, E.,
Galasso, F.,
SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based
3D Object Detection,
3DV20(771-780)
IEEE DOI
2102
Annotations, Detectors,
Adaptation models, Laser radar, Target tracking,
LiDAR data
BibRef
Krispel, G.,
Opitz, M.,
Waltner, G.,
Possegger, H.,
Bischof, H.,
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data,
WACV20(1863-1872)
IEEE DOI
2006
Laser radar, Task analysis, Sensors,
Laser beams, Fuses, Image segmentation
BibRef
Barrile, V.,
Candela, G.,
Fotia, A.,
Point Cloud Segmentation Using Image Processing Techniques For
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GEORES19(187-193).
DOI Link
1912
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Wang, W.,
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SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance
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CVPR18(2569-2578)
IEEE DOI
1812
Proposals, Image segmentation,
Semantics, Feature extraction
BibRef
Sharma, G.[Gopal],
Liu, D.[Difan],
Maji, S.[Subhransu],
Kalogerakis, E.[Evangelos],
Chaudhuri, S.[Siddhartha],
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Parsenet: A Parametric Surface Fitting Network for 3d Point Clouds,
ECCV20(VII:261-276).
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2011
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Honma, R.,
Date, H.,
Kanai, S.,
MLS Point Cloud Segmentation Based On Feature Points of Scanlines,
Laser19(1007-1013).
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1912
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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
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Walczak, J.[Jakub],
Wojciechowski, A.[Adam],
Clustering Quality Measures for Point Cloud Segmentation Tasks,
ICCVG18(173-186).
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1810
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Kuçak, R.A.,
Özdemir, E.,
Erol, S.,
The Segmentation of Point Clouds with K-means and ANN (Artifical Neural
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Hannover17(595-598).
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On the Repeatability of 3D Point Cloud Segmentation Based on Interest
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CRV12(9-16).
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Utilization of spatial information for point cloud segmentation,
3DTV10(1-4).
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1006
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Sedlacek, D.[David],
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Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction,
ISVC09(II: 218-227).
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Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Depth Object Detection, 3D Object Detection .