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.],
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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],
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Graph based over-segmentation methods for 3D point clouds,
CVIU(174), 2018, pp. 12-23.
Elsevier DOI
1812
3D point cloud over-segmentation, 3D point cloud segmentation,
Super-points, Grouping
BibRef
Zhao, B.F.[Bu-Fan],
Hua, X.H.[Xiang-Hong],
Yu, K.G.[Ke-Gen],
Xuan, W.[Wei],
Chen, X.J.[Xi-Jiang],
Tao, W.Y.[Wu-Yong],
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and
Improved Model Fitting,
GeoRS(58), No. 11, November 2020, pp. 7890-7907.
IEEE DOI
2011
Machine learning, Feature extraction,
Convolution, Laser modes, Shape, Fitting, 3-D point cloud,
segmentation
BibRef
Zhang, S.,
Cui, S.,
Ding, Z.,
Hypergraph Spectral Clustering for Point Cloud Segmentation,
SPLetters(27), 2020, pp. 1655-1659.
IEEE DOI
1806
Tensile stress, Frequency estimation,
Estimation, Covariance matrices, Laplace equations, Hypergraph,
spectral clustering
BibRef
Feng, M.T.[Ming-Tao],
Gilani, S.Z.[Syed Zulqarnain],
Wang, Y.N.[Yao-Nan],
Zhang, L.[Liang],
Mian, A.[Ajmal],
Relation Graph Network for 3D Object Detection in Point Clouds,
IP(30), 2021, pp. 92-107.
IEEE DOI
2011
Proposals, Object detection, Feature extraction, Laser radar,
deep learning
BibRef
Lei, H.[Huan],
Akhtar, N.[Naveed],
Mian, A.[Ajmal],
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds,
PAMI(43), No. 10, October 2021, pp. 3664-3680.
IEEE DOI
2109
BibRef
Earlier:
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical
Kernel,
CVPR20(11608-11617)
IEEE DOI
2008
BibRef
Earlier:
Octree Guided CNN With Spherical Kernels for 3D Point Clouds,
CVPR19(9623-9632).
IEEE DOI
2002
Kernel, Convolution, Neural networks,
Feature extraction, Semantics, semantic segmentation.
Convolutional codes, Integrated circuits, Robustness
BibRef
Hsu, P.H.[Pai-Hui],
Zhuang, Z.Y.[Zong-Yi],
Incorporating Handcrafted Features into Deep Learning for Point Cloud
Classification,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link
2011
BibRef
Li, X.[Xiaohan],
Chen, L.[Lu],
Li, S.[Shuang],
Zhou, X.[Xiang],
Depth segmentation in real-world scenes based on U-V disparity
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JVCIR(73), 2020, pp. 102920.
Elsevier DOI
2012
Depth scene segmentation, U-V disparity map,
Projection characteristics analysis, Object detection, RANSAC algorithm
BibRef
Guarda, A.F.R.[André F. R.],
Rodrigues, N.M.M.[Nuno M. M.],
Pereira, F.[Fernando],
Constant Size Point Cloud Clustering:
A Compact, Non-Overlapping Solution,
MultMed(23), 2021, pp. 77-91.
IEEE DOI
2012
Clustering algorithms, Clustering methods, Transform coding,
Encoding, Image segmentation, Complexity theory, Point cloud, non-overlapping
BibRef
Guarda, A.F.R.[André F. R.],
Rodrigues, N.M.M.[Nuno M. M.],
Pereira, F.[Fernando],
Neighborhood Adaptive Loss Function for Deep Learning-Based Point
Cloud Coding With Implicit and Explicit Quantization,
MultMedMag(28), No. 3, July 2021, pp. 107-116.
IEEE DOI
2109
Encoding, Deep learning, Distortion, Geometry, Machine learning,
Image coding, Point cloud coding, explicit quantization
BibRef
Tian, Y.F.[Yi-Fei],
Chen, L.[Long],
Song, W.[Wei],
Sung, Y.S.[Yun-Sick],
Woo, S.C.[Sang-Chul],
DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object
Recognition in Point Cloud,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Su, F.[Fei],
Zhu, H.H.[Hai-Hong],
Chen, T.Y.[Tao-Yi],
Li, L.[Lin],
Yang, F.[Fan],
Peng, H.X.[Hui-Xiang],
Tang, L.[Lei],
Zuo, X.K.[Xin-Kai],
Liang, Y.F.[Yi-Fan],
Ying, S.[Shen],
An anchor-based graph method for detecting and classifying indoor
objects from cluttered 3D point clouds,
PandRS(172), 2021, pp. 114-131.
Elsevier DOI
2101
Point cloud, Object classification, Functional part,
Graph matching, Super-graph, Graph similarity
BibRef
Wang, W.M.[Wei-Ming],
You, Y.[Yang],
Liu, W.[Wenhai],
Lu, C.[Cewu],
Point cloud classification with deep normalized Reeb graph
convolution,
IVC(106), 2021, pp. 104092.
Elsevier DOI
2102
Reeb graph, Point cloud, Graph normalization
BibRef
You, Y.[Yang],
Ye, Z.L.[Ze-Lin],
Lou, Y.J.[Yu-Jing],
Li, C.K.[Cheng-Kun],
Li, Y.L.[Yong-Lu],
Ma, L.Z.[Li-Zhuang],
Wang, W.M.[Wei-Ming],
Lu, C.[Cewu],
Canonical Voting: Towards Robust Oriented Bounding Box Detection in
3D Scenes,
CVPR22(1183-1192)
IEEE DOI
2210
Point cloud compression, Deep learning, Machine vision,
Object detection, Sensor systems and applications,
Vision applications and systems
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],
Cross-modal Weighting Network for RGB-D Salient Object Detection,
ECCV20(XVII:665-681).
Springer DOI
2011
BibRef
Zhang, X. .L.[Xin- Liang],
Fu, C.L.[Chen-Lin],
Zhao, Y.J.[Yun-Ji],
Xu, X.Z.[Xiao-Zhuo],
Hybrid feature CNN model for point cloud classification and
segmentation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4086-4091.
DOI Link
2103
BibRef
Wang, Q.[Qi],
Chen, J.[Jian],
Deng, J.Q.[Jian-Qiang],
Zhang, X.F.[Xin-Fang],
3D-CenterNet: 3D object detection network for point clouds with
center estimation priority,
PR(115), 2021, pp. 107884.
Elsevier DOI
2104
3D object detection, Point cloud, Deep learning
BibRef
Li, D.W.[Da-Wei],
Shi, G.L.[Guo-Liang],
Wu, Y.H.[Yu-Hao],
Yang, Y.P.[Yan-Ping],
Zhao, M.B.[Ming-Bo],
Multi-Scale Neighborhood Feature Extraction and Aggregation for Point
Cloud Segmentation,
CirSysVideo(31), No. 6, June 2021, pp. 2175-2191.
IEEE DOI
2106
Feature extraction, Semantics,
Image segmentation, Data mining,
point cloud segmentation
BibRef
Chen, C.F.[Chao-Fan],
Qian, S.S.[Sheng-Sheng],
Fang, Q.[Quan],
Xu, C.S.[Chang-Sheng],
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud
Segmentation,
MultMed(23), 2021, pp. 2335-2346.
IEEE DOI
2108
Feature extraction, Task analysis,
Layout, Logic gates, Machine learning,
gated graph attention network
BibRef
Zhu, J.F.[Jian-Feng],
Sui, L.C.[Li-Chun],
Zang, Y.[Yufu],
Zheng, H.[He],
Jiang, W.[Wei],
Zhong, M.[Mianqing],
Ma, F.[Fei],
Classification of Airborne Laser Scanning Point Cloud Using
Point-Based Convolutional Neural Network,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Brown, K.[Kyle],
Bourbakis, N.[Nikolaos],
Curve and Surface Fitting Techniques in Computer Vision,
IJIG(21), No. 4, October 2021 2021, pp. 2150041.
DOI Link
2110
BibRef
Guo, Y.L.[Yu-Lan],
Wang, H.Y.[Han-Yun],
Hu, Q.Y.[Qing-Yong],
Liu, H.[Hao],
Liu, L.[Li],
Bennamoun, M.[Mohammed],
Deep Learning for 3D Point Clouds: A Survey,
PAMI(43), No. 12, December 2021, pp. 4338-4364.
IEEE DOI
2112
Solid modeling, Deep learning,
Object detection, Laser radar, Task analysis, Sensors, Deep learning,
part segmentation
BibRef
Yang, F.[Fei],
Davoine, F.[Franck],
Wang, H.[Huan],
Jin, Z.[Zhong],
Continuous conditional random field convolution for point cloud
segmentation,
PR(122), 2022, pp. 108357.
Elsevier DOI
2112
Point cloud segmentation, Conditional random fields,
Message passing, Graph convolution, Mean-field approximation
BibRef
Zhang, J.[Jing],
Wang, J.J.[Jia-Jun],
Xu, D.[Da],
Li, Y.S.[Yun-Song],
HCNET: A Point Cloud Object Detection Network Based on Height and
Channel Attention,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Huang, H.[Hao],
Li, X.[Xiang],
Wang, L.J.[Ling-Jing],
Fang, Y.[Yi],
3D-MetaConNet: Meta-learning for 3D Shape Classification and
Segmentation,
3DV21(982-991)
IEEE DOI
2201
Training, Representation learning, Adaptation models,
Solid modeling, Shape, Computational modeling
BibRef
Li, X.Y.[Xing-Ye],
Zhang, L.[Ling],
Zhu, Z.G.[Zhi-Gang],
SnapshotNet: Self-supervised feature learning for point cloud data
segmentation using minimal labeled data,
CVIU(216), 2022, pp. 103339.
Elsevier DOI
2202
Self-supervision, Point cloud, Semantic segmentation
BibRef
Jing, W.P.[Wei-Peng],
Zhang, W.J.[Wen-Jun],
Li, L.H.[Lin-Hui],
Di, D.L.[Dong-Lin],
Chen, G.S.[Guang-Sheng],
Wang, J.[Jian],
AGNet: An Attention-Based Graph Network for Point Cloud
Classification and Segmentation,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Wang, F.[Fei],
Zhuang, Y.[Yan],
Zhang, H.[Hong],
Gu, H.[Hong],
Real-Time 3-D Semantic Scene Parsing With LiDAR Sensors,
Cyber(52), No. 3, March 2022, pp. 1351-1363.
IEEE DOI
2203
Convolution, Semantics, Real-time systems, Laser radar,
Tensile stress, Task analysis, 3-D convolutional neural network,
sparse (ST)
BibRef
Qiu, S.[Shi],
Anwar, S.[Saeed],
Barnes, N.[Nick],
Geometric Back-Projection Network for Point Cloud Classification,
MultMed(24), No. 2022, pp. 1943-1955.
IEEE DOI
2204
BibRef
Earlier:
Dense-Resolution Network for Point Cloud Classification and
Segmentation,
WACV21(3812-3821)
IEEE DOI
2106
Feature extraction, Task analysis, Geometry, Visualization, Shape,
Redundancy, Point Cloud Classification, 3D Deep Learning,
Error-correcting Feedback.
Training, Visualization, Adaptation models,
Computational modeling
BibRef
Li, Z.[Ziyu],
Yao, Y.[Yuncong],
Quan, Z.B.[Zhi-Bin],
Xie, J.[Jin],
Yang, W.K.[Wan-Kou],
Spatial information enhancement network for 3D object detection from
point cloud,
PR(128), 2022, pp. 108684.
Elsevier DOI
2205
3D object detection, Autonomous vehicles, Point cloud,
LiDAR sensor, 3D shape completion
BibRef
Wang, M.M.[Ming-Ming],
Chen, Q.[Qingkui],
Fu, Z.B.[Zhi-Bing],
LSNet: Learned Sampling Network for 3D Object Detection from Point
Clouds,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Zhao, C.[Chen],
Yang, J.Q.[Jia-Qi],
Xiong, X.[Xin],
Zhu, A.F.[Ang-Fan],
Cao, Z.G.[Zhi-Guo],
Li, X.[Xin],
Rotation invariant point cloud analysis:
Where local geometry meets global topology,
PR(127), 2022, pp. 108626.
Elsevier DOI
2205
Point cloud analysis, Rotation invariance, Deep learning,
Classification, Segmentation
BibRef
Xu, B.[Bo],
Chen, Z.[Zhen],
Zhu, Q.[Qing],
Ge, X.M.[Xu-Ming],
Huang, S.Z.[Sheng-Zhi],
Zhang, Y.T.[Ye-Ting],
Liu, T.Y.[Tian-Yang],
Wu, D.[Di],
Geometrical Segmentation of Multi-Shape Point Clouds Based on
Adaptive Shape Prediction and Hybrid Voting RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Wang, B.X.[Bing-Xu],
Lan, J.H.[Jin-Hui],
Gao, J.[Jiangjiang],
LiDAR Filtering in 3D Object Detection Based on Improved RANSAC,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Cetinkaya, B.[Bedrettin],
Kalkan, S.[Sinan],
Akbas, E.[Emre],
Does depth estimation help object detection?,
IVC(122), 2022, pp. 104427.
Elsevier DOI
2205
Object detection, Depth estimation, RGB-D
BibRef
Elich, C.[Cathrin],
Oswald, M.R.[Martin R.],
Pollefeys, M.[Marc],
Stueckler, J.[Joerg],
Weakly supervised learning of multi-object 3D scene decompositions
using deep shape priors,
CVIU(220), 2022, pp. 103440.
Elsevier DOI
2206
Multi-object 3D scene representation learning
BibRef
Song, Z.J.[Zhan-Jie],
Zhao, L.Q.[Lin-Qing],
Zhou, J.[Jie],
Learning Hybrid Semantic Affinity for Point Cloud Segmentation,
CirSysVideo(32), No. 7, July 2022, pp. 4599-4612.
IEEE DOI
2207
Semantics, Point cloud compression, Image segmentation,
Solid modeling, Task analysis, Learning systems,
graph convolutional network
BibRef
Tian, Y.L.[Yong-Lin],
Huang, L.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.[Bisheng],
Li, D.R.[De-Ren],
Wang, H.P.[Hai-Ping],
Cong, Y.Z.[Yang-Zi],
Hu, Z.[Zongtian],
CG-SSD: Corner guided single stage 3D object detection from LiDAR
point cloud,
PandRS(191), 2022, pp. 33-48.
Elsevier DOI
2208
LiDAR, Point clouds, 3D object detection, Deep learning
BibRef
Zhao, Y.H.[Yong-Heng],
Fang, G.C.[Guang-Chi],
Guo, Y.L.[Yu-Lan],
Guibas, L.J.[Leonidas J.],
Tombari, F.[Federico],
Birdal, T.[Tolga],
3DPointCaps++: Learning 3D Representations with Capsule Networks,
IJCV(130), No. 9, September 2022, pp. 2321-2336.
Springer DOI
2208
BibRef
Zhu, X.G.[Xin-Ge],
Zhou, H.[Hui],
Wang, T.[Tai],
Hong, F.Z.[Fang-Zhou],
Li, W.[Wei],
Ma, Y.X.[Yue-Xin],
Li, H.S.[Hong-Sheng],
Yang, R.G.[Rui-Gang],
Lin, D.[Dahua],
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based
Perception,
PAMI(44), No. 10, October 2022, pp. 6807-6822.
IEEE DOI
2209
BibRef
Earlier: A1, A2, A3, A4, A6, A5, A7, A9:
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR
Segmentation,
CVPR21(9934-9943)
IEEE DOI
2111
Laser radar, Convolution, Feature extraction, Gold, Task analysis,
Solid modeling, Cylindrical partition, asymmetrical convolution,
point cloud panoptic segmentation.
Solid modeling, Network topology, Interference, Encoding
BibRef
Cai, Q.[Qi],
Pan, Y.[Yingwei],
Yao, T.[Ting],
Mei, T.[Tao],
3D Cascade RCNN: High Quality Object Detection in Point Clouds,
IP(31), 2022, pp. 5706-5719.
IEEE DOI
2209
Proposals, Object detection, Point cloud compression, Detectors,
Training, Task analysis, Point cloud, 3D object detection, sample re-weighting
BibRef
Zhu, L.[Lei],
Chen, W.N.[Wei-Nan],
Lin, X.[Xubin],
He, L.[Li],
Guan, Y.[Yisheng],
Curvature-Variation-Inspired Sampling for Point Cloud Classification
and Segmentation,
SPLetters(29), 2022, pp. 1868-1872.
IEEE DOI
2209
Point cloud compression, Shape, Task analysis, Geometry,
Sampling methods, Convolution, Curvature variation,
point cloud
BibRef
Zheng, Y.[Yu],
Xu, X.[Xiuwei],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
PointRas:
Uncertainty-Aware Multi-Resolution Learning for Point Cloud Segmentation,
IP(31), 2022, pp. 6002-6016.
IEEE DOI
2209
Point cloud compression, Decoding, Signal resolution, Shape,
Interpolation, Semantics, Point cloud segmentation, contextual learning
BibRef
Honti, R.[Richard],
Erdélyi, J.[Ján],
Kopácik, A.[Alojz],
Semi-Automated Segmentation of Geometric Shapes from Point Clouds,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
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
BibRef
Luo, Z.[Ziwei],
Xie, Z.[Zhong],
Wan, J.[Jie],
Zeng, Z.[Ziyin],
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
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
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
Liang, H.[Hanxue],
Fan, H.[Hehe],
Fan, Z.W.[Zhi-Wen],
Wang, Y.[Yi],
Chen, T.[Tianlong],
Cheng, Y.[Yu],
Wang, Z.Y.[Zhang-Yang],
Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction,
ECCV22(III:156-172).
Springer DOI
2211
BibRef
Liu, L.[Linhu],
Tian, J.[Jiang],
Cheng, X.Q.[Xiang-Qian],
Shi, Z.C.[Zhong-Chao],
Fan, J.P.[Jian-Ping],
Rui, Y.[Yong],
Semi-Supervised 3D Medical Image Segmentation Via Boundary-Aware
Consistent Hidden Representation Learning,
ICIP22(836-840)
IEEE DOI
2211
Representation learning, Image segmentation,
Perturbation methods, Benchmark testing, Robustness, Decoding,
Semi-supervised segmentation
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.[Shuangjie],
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],
Ricci, E.[Elisa],
Poiesi, 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
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
Yin, J.[Junbo],
Fang, J.[Jin],
Zhou, D.[Dingfu],
Zhang, L.J.[Liang-Jun],
Xu, C.Z.[Cheng-Zhong],
Shen, J.B.[Jian-Bing],
Wang, W.[Wenguan],
Semi-supervised 3D Object Detection with Proficient Teachers,
ECCV22(XXXVIII:727-743).
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
Liu, K.C.[Kang-Cheng],
Zhao, Y.Z.[Yu-Zhi],
Nie, Q.[Qiang],
Gao, Z.[Zhi],
Chen, B.M.[Ben M.],
Weakly Supervised 3D Scene Segmentation with Region-Level Boundary
Awareness and Instance Discrimination,
ECCV22(XXVIII:37-55).
Springer DOI
2211
BibRef
Dong, S.C.[Shi-Chao],
Lin, G.S.[Guo-Sheng],
Hung, T.Y.[Tzu-Yi],
Learning Regional Purity for Instance Segmentation on 3D Point Clouds,
ECCV22(XXX:56-72).
Springer DOI
2211
BibRef
Ngo, T.[Tuan],
Nguyen, K.[Khoi],
Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance
Segmenter,
ECCV22(XXIX:561-578).
Springer DOI
2211
BibRef
Wu, Y.Z.[Yi-Zheng],
Shi, M.[Min],
Du, S.[Shuaiyuan],
Lu, H.[Hao],
Cao, Z.G.[Zhi-Guo],
Zhong, W.[Weicai],
3D Instances as 1D Kernels,
ECCV22(XXIX:235-252).
Springer DOI
2211
BibRef
Liu, G.[Guole],
Luo, Y.[Yaoru],
Yang, G.[Ge],
3d Particle Picking in Cryo-Electron Tomograms Using Instance
Segmentation,
ICIP22(2157-2161)
IEEE DOI
2211
Location awareness, Image segmentation, Solid modeling, Tomography,
Task analysis, Signal to noise ratio, Particle picking,
Gaussian-shaped masks
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.[Zizhao],
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, Z.[Ziyi],
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
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.[Tianyuan],
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
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
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.[Tianshu],
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
Unal, O.[Ozan],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Scribble-Supervised LiDAR Semantic Segmentation,
CVPR22(2687-2697)
IEEE DOI
2210
Point cloud compression, Training, Laser radar, Codes, Annotations,
Computational modeling, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Vu, T.[Thang],
Kim, K.[Kookhoi],
Luu, T.M.[Tung M.],
Nguyen, T.[Thanh],
Yoo, C.D.[Chang D.],
SoftGroup for 3D Instance Segmentation on Point Clouds,
CVPR22(2698-2707)
IEEE DOI
2210
Point cloud compression, Measurement, Codes, Semantics,
Pattern recognition, Segmentation, grouping and shape analysis
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
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
Liu, M.H.[Ming-Hua],
Zhou, Y.[Yin],
Qi, C.R.[Charles R.],
Gong, B.Q.[Bo-Qing],
Su, H.[Hao],
Anguelov, D.[Dragomir],
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds,
ECCV22(XXIX:70-89).
Springer DOI
2211
BibRef
Yi, L.[Li],
Gong, B.Q.[Bo-Qing],
Funkhouser, T.[Thomas],
Complete & 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, 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, Network architecture, Pattern recognition
BibRef
Chen, H.W.[Hai-Wei],
Liu, S.C.[Shi-Chen],
Chen, W.K.[Wei-Kai],
Li, H.[Hao],
Hill, R.[Randall],
Equivariant Point Network for 3D Point Cloud Analysis,
CVPR21(14509-14518)
IEEE DOI
2111
Convolutional codes, Visualization,
Shape, Convolution, Computational modeling
BibRef
Zheng, W.[Wu],
Tang, W.L.[Wei-Liang],
Jiang, L.[Li],
Fu, C.W.[Chi-Wing],
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud,
CVPR21(14489-14498)
IEEE DOI
2111
Training, Matched filters,
Shape, Detectors, Object detection
BibRef
Peng, X.D.[Xi-Dong],
Zhu, X.G.[Xin-Ge],
Wang, T.[Tai],
Ma, Y.X.[Yue-Xin],
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance
Depth Estimation,
WACV22(225-234)
IEEE DOI
2202
Costs, Laser radar,
Computational modeling, Estimation, Detectors, Scene Understanding
BibRef
Cheng, B.[Bowen],
Sheng, L.[Lu],
Shi, S.S.[Shao-Shuai],
Yang, M.[Ming],
Xu, D.[Dong],
Back-tracing Representative Points for Voting-based 3D Object
Detection in Point Clouds,
CVPR21(8959-8968)
IEEE DOI
2111
Location awareness, Visualization,
Object detection, Feature extraction, Pattern recognition, Proposals
BibRef
Qi, C.R.[Charles R.],
Zhou, Y.[Yin],
Najibi, M.[Mahyar],
Sun, P.[Pei],
Vo, K.[Khoa],
Deng, B.[Boyang],
Anguelov, D.[Dragomir],
Offboard 3D Object Detection from Point Cloud Sequences,
CVPR21(6130-6140)
IEEE DOI
2111
Training, Pipelines, Detectors,
Object detection, Semisupervised learning, Real-time systems
BibRef
Tian, H.[Hao],
Chen, Y.T.[Yun-Tao],
Dai, J.[Jifeng],
Zhang, Z.X.[Zhao-Xiang],
Zhu, X.[Xizhou],
Unsupervised Object Detection with LiDAR Clues,
CVPR21(5958-5968)
IEEE DOI
2111
Training, Location awareness, Image segmentation,
Laser radar, Annotations, Object detection
BibRef
Li, Z.C.[Zhi-Chao],
Wang, F.[Feng],
Wang, N.[Naiyan],
LiDAR R-CNN: An Efficient and Universal 3D Object Detector,
CVPR21(7542-7551)
IEEE DOI
2111
Laser radar, Costs,
Codes, Detectors, Real-time systems
BibRef
Fang, J.[Jin],
Zuo, X.X.[Xin-Xin],
Zhou, D.[Dingfu],
Jin, S.Z.[Sheng-Ze],
Wang, S.[Sen],
Zhang, L.J.[Liang-Jun],
LiDAR-Aug: A General Rendering-based Augmentation Framework for 3D
Object Detection,
CVPR21(4708-4718)
IEEE DOI
2111
Training, Laser radar, Neural networks,
Training data, Object detection, Detectors
BibRef
Aguilar, C.[Camilo],
Comer, M.[Mary],
Hanhan, I.[Imad],
Agyei, R.[Ronald],
Sangid, M.[Michael],
3D Fiber Segmentation with Deep Center Regression and Geometric
Clustering,
CVMI21(3741-3749)
IEEE DOI
2109
WWW Link. Geometry, Training,
Image color analysis, Shape, Microscopy, Neural networks
BibRef
Xiao, C.X.[Chen-Xi],
Wachs, J.[Juan],
Triangle-Net: Towards Robustness in Point Cloud Learning,
WACV21(826-835)
IEEE DOI
2106
Service robots,
Surveillance, Neural networks, Feature extraction, Robustness
BibRef
Yang, Y.R.[Yi-Rong],
Fan, B.[Bin],
Liu, Y.C.[Yong-Cheng],
Lin, H.[Hua],
Zhang, J.Y.[Ji-Yong],
Liu, X.[Xin],
Cai, X.Y.[Xin-Yu],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Deep Space Probing for Point Cloud Analysis,
ICPR21(10235-10242)
IEEE DOI
2105
Geometry, Convolution, Neural networks,
Benchmark testing, Convolutional neural networks
BibRef
Lin, H.[Hua],
Fan, B.[Bin],
Liu, Y.C.[Yong-Cheng],
Yang, Y.R.[Yi-Rong],
Pan, Z.[Zheng],
Shi, J.B.[Jian-Bo],
Pan, C.H.[Chun-Hong],
Xie, H.W.[Hui-Wen],
PointSpherical: Deep Shape Context for Point Cloud Learning in
Spherical Coordinates,
ICPR21(10266-10273)
IEEE DOI
2105
Solid modeling, Shape, Convolution, Semantics, Feature extraction
BibRef
Alliegro, A.[Antonio],
Boscaini, D.[Davide],
Tommasi, T.[Tatiana],
Joint Supervised and Self-Supervised Learning for 3D Real World
Challenges,
ICPR21(6718-6725)
IEEE DOI
2105
Solid modeling, Shape,
Transfer learning, Supervised learning, Intelligent agents
BibRef
Pan, Y.[Yunyi],
Gan, Y.[Yuan],
Liu, K.[Kun],
Zhang, Y.[Yan],
Progressive Scene Segmentation Based on Self-Attention Mechanism,
ICPR21(3985-3992)
IEEE DOI
2105
Convolution, Semantics, Benchmark testing, Decoding, Task analysis,
3D Scene Understanding
BibRef
Zhong, M.[Min],
Zeng, G.[Gang],
Enhanced Vote Network for 3D Object Detection in Point Clouds,
ICPR21(6624-6631)
IEEE DOI
2105
Aggregates, Face recognition,
Semantics, Object detection, Benchmark testing, Feature extraction
BibRef
Demilew, S.S.[Selameab S.],
Aghdam, H.H.[Hamed H.],
Laganičre, R.[Robert],
Petriu, E.M.[Emil M.],
FA3D: Fast and Accurate 3d Object Detection,
ISVC20(I:397-409).
Springer DOI
2103
BibRef
Krishna, O.[Onkar],
Irie, G.[Go],
Wu, X.[Xiaomeng],
Kawanishi, T.[Takahito],
Kashino, K.[Kunio],
Adaptive Spotting: Deep Reinforcement Object Search in 3d Point Clouds,
ACCV20(III:257-272).
Springer DOI
2103
BibRef
Zhang, Y.[Yi],
Ye, Y.[Yuwen],
Xiang, Z.Y.[Zhi-Yu],
Gu, J.Q.[Jia-Qi],
Sdp-net: Scene Flow Based Real-time Object Detection and Prediction
from Sequential 3d Point Clouds,
ACCV20(I:140-157).
Springer DOI
2103
BibRef
Liu, X.,
Cao, J.,
Bi, Q.,
Wang, J.,
Shi, B.,
Wei, Y.,
Dense Point Diffusion for 3D Object Detection,
3DV20(762-770)
IEEE DOI
2102
Convolution, Object detection,
Feature extraction, Task analysis, Quantization (signal)
BibRef
Saltori, C.,
Lathuiličre, S.,
Sebe, N.,
Ricci, E.,
Galasso, F.,
SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based
3D Object Detection,
3DV20(771-780)
IEEE DOI
2102
Annotations, Detectors,
Adaptation models, Laser radar, Target tracking,
LiDAR data
BibRef
Krispel, G.,
Opitz, M.,
Waltner, G.,
Possegger, H.,
Bischof, H.,
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data,
WACV20(1863-1872)
IEEE DOI
2006
Laser radar, Task analysis, Sensors,
Laser beams, Fuses, Image segmentation
BibRef
Barrile, V.,
Candela, G.,
Fotia, A.,
Point Cloud Segmentation Using Image Processing Techniques For
Structural Analysis,
GEORES19(187-193).
DOI Link
1912
BibRef
Wang, W.,
Yu, R.,
Huang, Q.,
Neumann, U.,
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance
Segmentation,
CVPR18(2569-2578)
IEEE DOI
1812
Proposals, Image segmentation,
Semantics, Feature extraction
BibRef
Sharma, G.[Gopal],
Liu, D.[Difan],
Maji, S.[Subhransu],
Kalogerakis, E.[Evangelos],
Chaudhuri, S.[Siddhartha],
Mech, R.[Radomír],
Parsenet: A Parametric Surface Fitting Network for 3d Point Clouds,
ECCV20(VII:261-276).
Springer DOI
2011
BibRef
Honma, R.,
Date, H.,
Kanai, S.,
MLS Point Cloud Segmentation Based On Feature Points of Scanlines,
Laser19(1007-1013).
DOI Link
1912
BibRef
Zhong, Z.,
Zhang, C.,
Liu, Y.,
Wu, Y.,
VIASEG: Visual Information Assisted Lightweight Point Cloud
Segmentation,
ICIP19(1500-1504)
IEEE DOI
1910
Point Cloud Segmentation, Cross-modality Fusion,
Fully Convolutional Residual Network
BibRef
Walczak, J.[Jakub],
Wojciechowski, A.[Adam],
Clustering Quality Measures for Point Cloud Segmentation Tasks,
ICCVG18(173-186).
Springer DOI
1810
BibRef
Kuçak, R.A.,
Özdemir, E.,
Erol, S.,
The Segmentation of Point Clouds with K-means and ANN (Artifical Neural
Network),
Hannover17(595-598).
DOI Link
1805
BibRef
Lam, J.[Joseph],
Greenspan, M.[Michael],
On the Repeatability of 3D Point Cloud Segmentation Based on Interest
Points,
CRV12(9-16).
IEEE DOI
1207
BibRef
Akman, O.[Oytun],
Bayramoglu, N.[Neslihan],
Alatan, A.A.[A. Aydin],
Jonker, P.P.[Pieter P.],
Utilization of spatial information for point cloud segmentation,
3DTV10(1-4).
IEEE DOI
1006
BibRef
Sedlacek, D.[David],
Zara, J.[Jiri],
Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction,
ISVC09(II: 218-227).
Springer DOI
0911
BibRef
Zhan, Q.M.[Qing-Ming],
Liang, Y.B.[Yu-Bin],
Xiao, Y.H.[Ying-Hui],
Color-Based Segmentation of Point Clouds,
Laser09(248).
0909
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Depth Object Detection, 3D Object Detection .