11.2.4.1 Depth Object Segmentation, Point Cloud Segmentation

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

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

Poux, F.[Florent], Billen, R.[Roland],
Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Li, Y., Ma, L., Zhong, Z., Cao, D., Li, J.,
TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation,
GeoRS(58), No. 5, May 2020, pp. 3588-3600.
IEEE DOI 2005
Feature extraction, Convolution, Semantics, Kernel, Correlation, Task analysis, Deep learning, semantic segmentation 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],
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, Convolutional codes, Integrated circuits, Robustness, Computer architecture BibRef

Hsu, P.H.[Pai-Hui], Zhuang, Z.Y.[Zong-Yi],
Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Li, X.[Xiaohan], Chen, L.[Lu], Li, S.[Shuang], Zhou, X.[Xiang],
Depth segmentation in real-world scenes based on U-V disparity analysis,
JVCIR(73), 2020, pp. 102920.
Elsevier DOI 2012
Depth scene segmentation, U-V disparity map, Projection characteristics analysis, Object detection, RANSAC algorithm BibRef

Guarda, A.F.R.[André F. R.], Rodrigues, N.M.M.[Nuno M. M.], Pereira, F.[Fernando],
Constant Size Point Cloud Clustering: A Compact, Non-Overlapping Solution,
MultMed(23), 2021, pp. 77-91.
IEEE DOI 2012
Clustering algorithms, Clustering methods, Transform coding, Encoding, Image segmentation, Complexity theory, Point cloud, non-overlapping BibRef

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


Widyaningrum, E., Fajari, M.K., Lindenbergh, R.C., Hahn, M.,
Tailored Features for Semantic Segmentation with A DGCNN Using Free Training Samples of A Colored Airborne Point Cloud,
ISPRS20(B2:339-346).
DOI Link 2012
BibRef

Leichter, A., Werner, M., Sester, M.,
Feature-Extraction from All-scale Neighborhoods with Applications To Semantic Segmentation of Point Clouds,
ISPRS20(B2:263-270).
DOI Link 2012
BibRef

Zhang, F.H.[Fei-Hu], Fang, J.[Jin], Wah, B.[Benjamin], Torr, P.[Philip],
Deep Fusionnet for Point Cloud Semantic Segmentation,
ECCV20(XXIV:644-663).
Springer DOI 2012
BibRef

He, T.[Tong], Gong, D.[Dong], Tian, Z.[Zhi], Shen, C.H.[Chun-Hua],
Learning and Memorizing Representative Prototypes for 3d Point Cloud Semantic and Instance Segmentation,
ECCV20(XVIII:564-580).
Springer DOI 2012
BibRef

Liu, J.X.[Jin-Xian], Yu, M.H.[Ming-Hui], Ni, B.B.[Bing-Bing], Chen, Y.[Ye],
Self-prediction for Joint Instance and Semantic Segmentation of Point Clouds,
ECCV20(XXII:187-204).
Springer DOI 2011
BibRef

Wong, C.C.[Chi-Chong], Vong, C.M.[Chi-Man],
Efficient Outdoor 3d Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts,
ECCV20(XXVII:499-514).
Springer DOI 2011
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

Du, A.[Anan], Pang, S.C.[Shu-Chao], Huang, X.S.[Xiao-Shui], Zhang, J.[Jian], Wu, Q.A.[Qi-Ang],
Exploring Long-Short-Term Context For Point Cloud Semantic Segmentation,
ICIP20(2755-2759)
IEEE DOI 2011
Task analysis, Semantics, Decoding, Feature extraction, Context modeling, Training, point cloud, long-short-term context BibRef

Xu, X., Lee, G.H.,
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels,
CVPR20(13703-13712)
IEEE DOI 2008
Task analysis, Image color analysis, Training, Shape, Semantics, Labeling 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

Wang, L.[Lei], Huang, Y.[Yuchun], Hou, Y.[Yaolin], Zhang, S.[Shenman], Shan, J.[Jie],
Graph Attention Convolution for Point Cloud Semantic Segmentation,
CVPR19(10288-10297).
IEEE DOI 2002
BibRef

Adam, A., Grammatikopoulos, L., Karras, G., Protopapadakis, E., Karantzalos, K.,
A Semantic 3d Point Cloud Segmentation Approach Based On Optimal View Selection for 2d Image Feature Extraction,
LC3D19(9-14).
DOI Link 1912
BibRef

Barrile, V., Candela, G., Fotia, A.,
Point Cloud Segmentation Using Image Processing Techniques For Structural Analysis,
GEORES19(187-193).
DOI Link 1912
BibRef

Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Xie, L.H.[Lin-Hai], Rosa, S.[Stefano], Guo, Y.L.[Yu-Lan], Wang, Z.H.[Zhi-Hua], Trigoni, N.[Niki], Markham, A.[Andrew],
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds,
CVPR20(11105-11114)
IEEE DOI 2008
Semantics, Feature extraction, Task analysis, Encoding, Computer architecture, Benchmark testing BibRef

Ma, Y.X.[Yan-Xin], Guo, Y.L.[Yu-Lan], Lei, Y.J.[Yin-Jie], Lu, M.[Min], Zhang, J.[Jun],
3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation,
ICPR18(1560-1566)
IEEE DOI 1812
Feature extraction, Semantics, Labeling, Neural networks, Task analysis BibRef

Landrieu, L., Simonovsky, M.,
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs,
CVPR18(4558-4567)
IEEE DOI 1812
Shape, Semantics, Image segmentation, Image edge detection, Pipelines 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

Biasutti, P., Lepetit, V., Aujol, J., Brédif, M., Bugeau, A.,
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net,
CVRSUAD19(942-950)
IEEE DOI 2004
feature extraction, graphics processing units, image segmentation, optical radar, radar imaging, LU-Net, deep learning BibRef

Honma, R., Date, H., Kanai, S.,
MLS Point Cloud Segmentation Based On Feature Points of Scanlines,
Laser19(1007-1013).
DOI Link 1912
BibRef

Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C., Jia, J.,
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation,
ICCV19(10432-10440)
IEEE DOI 2004
graph theory, image colour analysis, image segmentation, message passing, object detection, Labeling BibRef

Zhong, Z., Zhang, C., Liu, Y., Wu, Y.,
VIASEG: Visual Information Assisted Lightweight Point Cloud Segmentation,
ICIP19(1500-1504)
IEEE DOI 1910
Point Cloud Segmentation, Cross-modality Fusion, Fully Convolutional Residual Network BibRef

Walczak, J.[Jakub], Wojciechowski, A.[Adam],
Clustering Quality Measures for Point Cloud Segmentation Tasks,
ICCVG18(173-186).
Springer DOI 1810
BibRef

Kuçak, R.A., Özdemir, E., Erol, S.,
The Segmentation of Point Clouds with K-means and ANN (Artifical Neural Network),
Hannover17(595-598).
DOI Link 1805
BibRef

Lam, J.[Joseph], Greenspan, M.[Michael],
On the Repeatability of 3D Point Cloud Segmentation Based on Interest Points,
CRV12(9-16).
IEEE DOI 1207
BibRef

Akman, O.[Oytun], Bayramoglu, N.[Neslihan], Alatan, A.A.[A. Aydin], Jonker, P.P.[Pieter P.],
Utilization of spatial information for point cloud segmentation,
3DTV10(1-4).
IEEE DOI 1006
BibRef

Sedlacek, D.[David], Zara, J.[Jiri],
Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction,
ISVC09(II: 218-227).
Springer DOI 0911
BibRef

Zhan, Q.M.[Qing-Ming], Liang, Y.B.[Yu-Bin], Xiao, Y.H.[Ying-Hui],
Color-Based Segmentation of Point Clouds,
Laser09(248). 0909
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Depth Object Detection, 3D Object Detection .


Last update:Jan 17, 2021 at 16:22:28