11.2.4.5 Semantic Object Detection, 3D, Depth

Chapter Contents (Back)
Semantic Segmentation. Object Detection. Semantic Object Detection.

Thonnat, M.,
Semantic Interpretation of 3-D Stereo Data: Finding the Main Structures,
PRAI(2), 1988, pp. 509-525. BibRef 8800
Earlier: ICPR86(1051-1054). BibRef

Ma, R., Thonnat, M.,
Object Detection in Outdoor Scenes by Disparity Map Segmentation,
ICPR92(I:546-549).
IEEE DOI BibRef 9200

Boochs, F.[Frank], Kern, F.[Fredie], Schütze, R.[Rainer], Marbs, A.[Andreas],
Approaches for geometrical and semantic modelling of huge unstructured 3D point clouds,
PFG(2009), No. 1, 2009, pp. 65-77.
WWW Link. 1211
BibRef

Su, W.[Wen], Wang, Z.F.[Zeng-Fu],
Widening residual skipped network for semantic segmentation,
IET-IPR(11), No. 10, October 2017, pp. 880-887.
DOI Link 1710
BibRef
Earlier:
Regularized fully convolutional networks for RGB-D semantic segmentation,
VCIP16(1-4)
IEEE DOI 1701
Brightness BibRef

Pagnutti, G.[Giampaolo], Minto, L.[Ludovico], Zanuttigh, P.[Pietro],
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting,
IET-CV(11), No. 8, December 2017, pp. 633-642.
DOI Link 1712
BibRef

Guo, Y.R.[Yan-Rong], Chen, T.[Tao],
Semantic segmentation of RGBD images based on deep depth regression,
PRL(109), 2018, pp. 55-64.
Elsevier DOI 1806
Deep depth regression, RGBD semantic segmentation, Convolutional neural network, Fully convolutional network BibRef

Sun, Y.[Ying], Zhang, X.C.[Xin-Chang], Xin, Q.C.[Qin-Chuan], Huang, J.F.[Jian-Feng],
Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data,
PandRS(143), 2018, pp. 3-14.
Elsevier DOI 1808
LiDAR, High-resolution imagery, Multi-modal fusion, Multi-resolution segmentation, Semantic segmentation BibRef

Ponciano, J.J.[Jean-Jacques], Trémeau, A.[Alain], Boochs, F.[Frank],
Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Boochs, F.[Frank], Karmacharya, A., Marbs, A.,
Knowledge-based Object Detection In Laser Scanning Point Clouds,
ISPRS12(XXXIX-B3:91-96).
DOI Link 1209
BibRef

Truong, H.Q.[Hung Quoc], Ben Hmida, H.[Helmi], Marbs, A.[Andreas], Boochs, F.[Frank],
Integration of knowledge into the detection of objects in point clouds,
PCVIA10(B:143).
PDF File. 1009
BibRef

Aytaylan, H.[Hakan], Yuksel, S.E.[Seniha Esen],
Fully-connected semantic segmentation of hyperspectral and LiDAR data,
IET-CV(13), No. 3, April 2019, pp. 285-293.
DOI Link 1904
BibRef

Ge, X.M.[Xu-Ming], Wu, B.[Bo], Li, Y.[Yuan], Hu, H.[Han],
A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Li, Y.[Yong], Chen, D.[Dong], Du, X.[Xiance], Xia, S.[Shaobo], Wang, Y.L.[Yu-Liang], Xu, S.[Sheng], Yang, Q.A.[Qi-Ang],
Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Fang, H.[Hao], Lafarge, F.[Florent],
Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information,
PandRS(154), 2019, pp. 246-258.
Elsevier DOI 1907
Point cloud, Semantic segmentation, Deep learning, Multi-scale contextual information 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

Zhou, H.[Heng], Fang, Z.J.[Zhi-Jun], Gao, Y.B.[Yong-Bin], Huang, B.[Bo], Zhong, C.[Cengsi], Shang, R.[Ruoxi],
Feature fusion network based on attention mechanism for 3D semantic segmentation of point clouds,
PRL(133), 2020, pp. 327-333.
Elsevier DOI 2005
3D Semantic segmentation, Point clouds, Feature fusion, Attention mechanism BibRef

Lin, Y.P.[Ya-Ping], Vosselman, G.[George], Cao, Y.P.[Yan-Peng], Yang, M.Y.[Michael Ying],
Active and incremental learning for semantic ALS point cloud segmentation,
PandRS(169), 2020, pp. 73-92.
Elsevier DOI 2011
Point clouds, Semantic segmentation, Active learning, Incremental learning, Deep learning BibRef

Lin, D.[Di], Huang, H.[Hui],
Zig-Zag Network for Semantic Segmentation of RGB-D Images,
PAMI(42), No. 10, October 2020, pp. 2642-2655.
IEEE DOI 2009
Image segmentation, Semantics, Computer architecture, Decoding, Image resolution, Feature extraction, Correlation, RGB-D images, convolutional neural networks BibRef

Lin, D.[Di], Chen, G.Y.[Guang-Yong], Cohen-Or, D.[Daniel], Heng, P.A.[Pheng-Ann], Huang, H.[Hui],
Cascaded Feature Network for Semantic Segmentation of RGB-D Images,
ICCV17(1320-1328)
IEEE DOI 1802
feature extraction, feedforward neural nets, image colour analysis, image representation, image segmentation, Visualization BibRef

Han, X.[Xu], Dong, Z.[Zhen], Yang, B.[Bisheng],
A point-based deep learning network for semantic segmentation of MLS point clouds,
PandRS(175), 2021, pp. 199-214.
Elsevier DOI 2105
Point cloud, 3D deep learning, Semantic segmentation, Feature aggregation, Unbalanced classes BibRef

Jiang, B.[Bo], Zhou, Z.[Zitai], Wang, X.[Xiao], Tang, J.[Jin], Luo, B.[Bin],
cmSalGAN: RGB-D Salient Object Detection With Cross-View Generative Adversarial Networks,
MultMed(23), 2021, pp. 1343-1353.
IEEE DOI 2105
Saliency detection, Feature extraction, Object detection, Generative adversarial networks, Fuses, Multi-view Learning BibRef

Chen, L.Z., Lin, Z., Wang, Z., Yang, Y.L., Cheng, M.M.,
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation,
IP(30), 2021, pp. 2313-2324.
IEEE DOI 2102
convolutional neural nets, geometry, image colour analysis, image segmentation, stereo image processing, RGBD semantic segmentation BibRef

Zhang, G.D.[Guo-Dong], Xue, J.H.[Jing-Hao], Xie, P.W.[Peng-Wei], Yang, S.[Sifan], Wang, G.J.[Gui-Jin],
Non-Local Aggregation for RGB-D Semantic Segmentation,
SPLetters(28), 2021, pp. 658-662.
IEEE DOI 2104
Semantics, Feature extraction, Manganese, Interpolation, Image segmentation, Benchmark testing, Training, RGB-D semantic segmentation BibRef

Gao, Q.[Qian], Shen, X.[Xukun],
ThickSeg: Efficient semantic segmentation of large-scale 3D point clouds using multi-layer projection,
IVC(108), 2021, pp. 104161.
Elsevier DOI 2104
3D point cloud, Semantic segmentation, Convolutional neural network, Large scale BibRef

Kwak, J.[Jeonghoon], Sung, Y.[Yunsick],
DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ponciano, J.J.[Jean-Jacques], Roetner, M.[Moritz], Reiterer, A.[Alexander], Boochs, F.[Frank],
Object Semantic Segmentation in Point Clouds: Comparison of a Deep Learning and a Knowledge-Based Method,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Shuai, H.[Hui], Xu, X.[Xiang], Liu, Q.S.[Qing-Shan],
Backward Attentive Fusing Network With Local Aggregation Classifier for 3D Point Cloud Semantic Segmentation,
IP(30), 2021, pp. 4973-4984.
IEEE DOI 2106
Semantics, Feature extraction, Decoding, Iron, Noise measurement, Aggregates, local aggregation classifier BibRef

Liu, W.[Wei], Luo, Z.M.[Zhi-Ming], Cai, Y.Z.[Yuan-Zheng], Yu, Y.[Ying], Ke, Y.[Yang], Junior, J.M.[José Marcato], Gonçalves, W.N.[Wesley Nunes], Li, J.[Jonathan],
Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning,
PandRS(176), 2021, pp. 211-221.
Elsevier DOI 2106
Semantic segmentation, Point cloud, Domain adaptation, Adversarial learning, Multi-modal learning BibRef

Lin, Y.P.[Ya-Ping], Vosselman, G.[George], Cao, Y.[Yanpeng], Yang, M.Y.[Michael Ying],
Local and global encoder network for semantic segmentation of Airborne laser scanning point clouds,
PandRS(176), 2021, pp. 151-168.
Elsevier DOI 2106
Point clouds, Semantic segmentation, Global context, Attention models BibRef

Xiao, A.[Aoran], Yang, X.F.[Xiao-Fei], Lu, S.[Shijian], Guan, D.[Dayan], Huang, J.X.[Jia-Xing],
FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation,
PandRS(176), 2021, pp. 237-249.
Elsevier DOI 2106
LiDAR, Point cloud, Semantic segmentation, Spherical projection, Autonomous driving, Scene understanding BibRef

Laupheimer, D.[Dominik], Haala, N.[Norbert],
Juggling with representations: On the information transfer between imagery, point clouds, and meshes for multi-modal semantics,
PandRS(176), 2021, pp. 55-68.
Elsevier DOI 2106
Multi-modality, Data fusion, 3D textured mesh, 3D point cloud, Imagery, Ground truth, Semantic segmentation BibRef

Krisanski, S.[Sean], Taskhiri, M.S.[Mohammad Sadegh], Aracil, S.G.[Susana Gonzalez], Herries, D.[David], Turner, P.[Paul],
Sensor Agnostic Semantic Segmentation of Structurally Diverse and Complex Forest Point Clouds Using Deep Learning,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Yue, Y.[Yuchun], Zhou, W.[Wujie], Lei, J.S.[Jing-Sheng], Yu, L.[Lu],
Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images,
SPLetters(28), 2021, pp. 1115-1119.
IEEE DOI 2106
Semantics, Image segmentation, Feature extraction, Decoding, Sun, Computer architecture, Training, Deep learning, RGB-d image, multilevel feature fusion BibRef

Lamas, D.[Daniel], Soilán, M.[Mario], Grandío, J.[Javier], Riveiro, B.[Belén],
Automatic Point Cloud Semantic Segmentation of Complex Railway Environments,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Liu, H.[Hao], Guo, Y.L.[Yu-Lan], Ma, Y.N.[Yan-Ni], Lei, Y.J.[Yin-Jie], Wen, G.J.[Gong-Jian],
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation,
MultMed(23), 2021, pp. 2045-2055.
IEEE DOI 2107
Semantics, Image segmentation, Encoding, Convolution, semantic context BibRef

Ma, Y.N.[Yan-Ni], Guo, Y.L.[Yu-Lan], Liu, H.[Hao], Lei, Y.J.[Yin-Jie], Wen, G.J.[Gong-Jian],
Global Context Reasoning for Semantic Segmentation of 3D Point Clouds,
WACV20(2920-2929)
IEEE DOI 2006
Semantics, Cognition, Convolution, Feature extraction, Task analysis 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

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

Rim, B.[Beanbonyka], Lee, A.[Ahyoung], Hong, M.[Min],
Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder-Decoder Shared MLPs with Multiple Losses,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liu, L.M.[Li-Man], Yu, J.[Jinjin], Tan, L.[Longyu], Su, W.J.[Wan-Juan], Zhao, L.[Lin], Tao, W.B.[Wen-Bing],
Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhou, W.[Wujie], Yuan, J.Z.[Jian-Zhong], Lei, J.S.[Jing-Sheng], Luo, T.[Ting],
TSNet: Three-Stream Self-Attention Network for RGB-D Indoor Semantic Segmentation,
IEEE_Int_Sys(36), No. 4, July 2021, pp. 73-78.
IEEE DOI 2109
Semantics, Convolution, Feature extraction, Image segmentation, Streaming media, Spatial resolution, Data mining, RGB-D, indoor semantic segmentation BibRef

Zhao, Y.F.[Yi-Fan], Zhao, J.W.[Jia-Wei], Li, J.[Jia], Chen, X.W.[Xiao-Wu],
RGB-D Salient Object Detection With Ubiquitous Target Awareness,
IP(30), 2021, pp. 7717-7731.
IEEE DOI 2109
Object detection, Feature extraction, Fuses, Task analysis, Logic gates, Estimation, Image edge detection, ubiquitous target awareness BibRef

Zhou, W.J.[Wu-Jie], Liu, J.F.[Jin-Fu], Lei, J.S.[Jing-Sheng], Yu, L.[Lu], Hwang, J.N.[Jenq-Neng],
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation,
IP(30), 2021, pp. 7790-7802.
IEEE DOI 2109
Image segmentation, Semantics, Feature extraction, Decoding, Temperature sensors, Robot sensing systems, Motion segmentation, refinement strategy BibRef

Feng, M.T.[Ming-Tao], Zhang, L.[Liang], Lin, X.F.[Xue-Fei], Gilani, S.Z.[Syed Zulqarnain], Mian, A.[Ajmal],
Point attention network for semantic segmentation of 3D point clouds,
PR(107), 2020, pp. 107446.
Elsevier DOI 2008
Semantic segmentation, 3D point cloud, Point attention network, Deep learning BibRef

Ibrahim, M.[Muhammad], Akhtar, N.[Naveed], Ullah, K.[Khalil], Mian, A.[Ajmal],
Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Gu, Z.X.[Zhang-Xuan], Niu, L.[Li], Zhao, H.[Haohua], Zhang, L.Q.[Li-Qing],
Hard Pixel Mining for Depth Privileged Semantic Segmentation,
MultMed(23), 2021, pp. 3738-3751.
IEEE DOI 2110
Semantics, Image segmentation, Training, Task analysis, Fuses, Measurement uncertainty, Testing, Semantic segmentation, RGBD semantic segmentation BibRef


Klingner, M.[Marvin], Bär, A.[Andreas], Mross, M.[Marcel], Fingscheidt, T.[Tim],
Improving Online Performance Prediction for Semantic Segmentation,
SAIAD21(1-11)
IEEE DOI 2109
Training, Laser radar, Semantics, Estimation, Virtual reality, Prediction algorithms, Decoding BibRef

Unal, O.[Ozan], Van Gool, L.J.[Luc J.], Dai, D.X.[Deng-Xin],
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection,
WACV21(2949-2958)
IEEE DOI 2106
Location awareness, Training, Image segmentation, Semantics, Pipelines, Estimation BibRef

Alnaggar, Y.A.[Yara Ali], Afifi, M.[Mohamed], Amer, K.[Karim], ElHelw, M.[Mohamed],
Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds,
WACV21(1799-1808)
IEEE DOI 2106
Laser radar, Semantics, Real-time systems, Sensors BibRef

Zhang, Y.F.[Yi-Fei], Sidibé, D.[Désiré], Morel, O.[Olivier], Meriaudeau, F.[Fabrice],
Incorporating Depth Information into Few-Shot Semantic Segmentation,
ICPR21(3582-3588)
IEEE DOI 2105
Measurement, Image segmentation, Visualization, Image color analysis, Fuses, Semantics, Neural networks BibRef

Zhong, M.[Min], Zeng, G.[Gang],
Joint Semantic-Instance Segmentation of 3D Point Clouds: Instance Separation and Semantic Fusion,
ICPR21(6616-6623)
IEEE DOI 2105
Measurement, Fuses, Semantics, Pattern recognition BibRef

Jaritz, M., Vu, T., de Charette, R., Wirbel, E., Pérez, P.,
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation,
CVPR20(12602-12611)
IEEE DOI 2008
Semantics, Image segmentation, Task analysis, Laser radar, Training BibRef

Sun, W.X.[Wei-Xuan], Zhang, J.[Jing], Barnes, N.[Nick],
3D Guided Weakly Supervised Semantic Segmentation,
ACCV20(I:585-602).
Springer DOI 2103
BibRef

Wu, G.N.[Guang-Nan], Pan, Z.Y.[Zhi-Yi], Jiang, P.[Peng], Tu, C.H.[Chang-He],
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds,
ACCV20(I:209-226).
Springer DOI 2103
BibRef

Kölle, M.[Michael], Walter, V.[Volker], Schmohl, S.[Stefan], Soergel, U.[Uwe],
Remembering Both the Machine and the Crowd When Sampling Points: Active Learning for Semantic Segmentation of ALS Point Clouds,
PRRS20 (505-520).
Springer DOI 2103
BibRef

Cortinhal, T.[Tiago], Tzelepis, G.[George], Aksoy, E.E.[Eren Erdal],
Salsanext: Fast, Uncertainty-aware Semantic Segmentation of Lidar Point Clouds,
ISVC20(II:207-222).
Springer DOI 2103
BibRef

Akadas, K.[Kiran], Gangisetty, S.[Shankar],
3d Semantic Segmentation for Large-scale Scene Understanding,
MLCSA20(87-102).
Springer DOI 2103
BibRef

Wang, X., Fan, X., Zhao, D.,
A semantic labeling framework for ALS point clouds based on discretization and CNN,
VCIP20(58-61)
IEEE DOI 2102
Semantics, Labeling, Entropy, Neural networks, Microprocessors, Computer architecture, CNN BibRef

Duerr, F., Pfaller, M., Weigel, H., Beyerer, J.,
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment,
3DV20(781-790)
IEEE DOI 2102
Image segmentation, Feature extraction, Semantics, Sensors, Laser radar, Task analysis, Point clouds BibRef

Yu, M., Liu, J., Ni, B., Li, C.,
Two-Stage Relation Constraint for Semantic Segmentation of Point Clouds,
3DV20(271-280)
IEEE DOI 2102
Semantics, Task analysis, Convolution, Computer architecture, Heuristic algorithms, Training, Semantic Segmentation 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

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

Hu, Z.[Zeyu], Zhen, M.M.[Ming-Min], Bai, X.Y.[Xu-Yang], Fu, H.B.[Hong-Bo], Tai, C.L.[Chiew-Lan],
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds,
ECCV20(XX:222-239).
Springer DOI 2011
BibRef

Zhu, S., Brazil, G., Liu, X.,
The Edge of Depth: Explicit Constraints Between Segmentation and Depth,
CVPR20(13113-13122)
IEEE DOI 2008
Semantics, Estimation, Image segmentation, Image edge detection, Cameras, Training, Hemorrhaging BibRef

Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Gong, B., Foroosh, H.,
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation,
CVPR20(9598-9607)
IEEE DOI 2008
Laser radar, Semantics, Image segmentation, Neural networks, Task analysis BibRef

Wang, L., Li, X., Fang, Y.,
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation,
CVPR20(4503-4512)
IEEE DOI 2008
Shape, Solid modeling, Neural networks, Training, Semantics, Supervised learning BibRef

Shi, H., Lin, G., Wang, H., Hung, T., Wang, Z.,
SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds,
CVPR20(4573-4582)
IEEE DOI 2008
Semantics, Convolution, Feature extraction, Image segmentation, Task analysis, Videos BibRef

Kundu, A.[Abhijit], Yin, X.Q.[Xiao-Qi], Fathi, A.[Alireza], Ross, D.[David], Brewington, B.[Brian], Funkhouser, T.[Thomas], Pantofaru, C.[Caroline],
Virtual Multi-view Fusion for 3d Semantic Segmentation,
ECCV20(XXIV:518-535).
Springer DOI 2012
BibRef

Chen, X.K.[Xiao-Kang], Lin, K.Y.[Kwan-Yee], Wang, J.[Jingbo], Wu, W.[Wayne], Qian, C.[Chen], Li, H.S.[Hong-Sheng], Zeng, G.[Gang],
Bi-directional Cross-modality Feature Propagation with Separation-and-aggregation Gate for RGB-D Semantic Segmentation,
ECCV20(XI:561-577).
Springer DOI 2011
BibRef

Wei, J., Lin, G., Yap, K., Hung, T., Xie, L.,
Multi-Path Region Mining for Weakly Supervised 3D Semantic Segmentation on Point Clouds,
CVPR20(4383-4392)
IEEE DOI 2008
Task analysis, Semantics, Image segmentation, Machine learning, Aggregates BibRef

Li, J., Han, K., Wang, P., Liu, Y., Yuan, X.,
Anisotropic Convolutional Networks for 3D Semantic Scene Completion,
CVPR20(3348-3356)
IEEE DOI 2008
Convolution, Semantics, Kernel, Feature extraction, Adaptation models, Context modeling BibRef

Li, J.[Jie], Liu, Y.[Yu], Gong, D.[Dong], Shi, Q.F.[Qin-Feng], Yuan, X.[Xia], Zhao, C.X.[Chun-Xia], Reid, I.D.[Ian D.],
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion,
CVPR19(7685-7694).
IEEE DOI 2002
BibRef

Malinverni, E.S., Pierdicca, R., Paolanti, M., Martini, M., Morbidoni, C., Matrone, F., Lingua, A.,
Deep Learning for Semantic Segmentation of 3d Point Cloud,
CIPA19(735-742).
DOI Link 1912
BibRef

Chen, Y.[Yunlu], Mensink, T.[Thomas], Gavves, E.[Efstratios],
3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation,
3DV19(173-182)
IEEE DOI 1911
Convolution, Semantics, Kernel, Solid modeling, Image segmentation, local convolution BibRef

Hung, S., Lo, S., Hang, H.,
Incorporating Luminance, Depth and Color Information by a Fusion-Based Network for Semantic Segmentation,
ICIP19(2374-2378)
IEEE DOI 1910
RGB-D semantic segmentation, depth map, illuminance, fusion-based network BibRef

Xing, Y., Wang, J., Chen, X., Zeng, G.,
2.5D Convolution for RGB-D Semantic Segmentation,
ICIP19(1410-1414)
IEEE DOI 1910
RGB-D Semantic Segmentation, Convoutional Neural Networks, Geometry in CNN BibRef

Hu, X., Yang, K., Fei, L., Wang, K.,
ACNET: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation,
ICIP19(1440-1444)
IEEE DOI 1910
Attention, Complementary, RGBD semantic segmentation BibRef

Hou, J.[Ji], Dai, A.[Angela], Niessner, M.[Matthias],
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans,
CVPR19(4416-4425).
IEEE DOI 2002
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

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

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

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

Dai, A.[Angela], Nießner, M.[Matthias],
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation,
ECCV18(X: 458-474).
Springer DOI 1810
BibRef

Engelmann, F.[Francis], Kontogianni, T.[Theodora], Schult, J.[Jonas], Leibe, B.[Bastian],
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds,
DeepLearn-G18(III:395-409).
Springer DOI 1905
BibRef

Piewak, F.[Florian], Pinggera, P.[Peter], Schäfer, M.[Manuel], Peter, D.[David], Schwarz, B.[Beate], Schneider, N.[Nick], Enzweiler, M.[Markus], Pfeiffer, D.[David], Zöllner, M.[Marius],
Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation,
MultLearnApp18(VI:497-513).
Springer DOI 1905
BibRef

Graham, B., Engelcke, M., van der Maaten, L.[Laurens],
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks,
CVPR18(9224-9232)
IEEE DOI 1812
Convolution, Memory management, Convolutional codes, Stationary state, Semantics, Image segmentation BibRef

Thomas, H., Goulette, F., Deschaud, J., Marcotegui, B.,
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods,
3DV18(390-398)
IEEE DOI 1812
geometry, learning (artificial intelligence), nearest neighbour methods, pattern classification, Segmentation BibRef

Zhang, C., Luo, W., Urtasun, R.,
Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds,
3DV18(399-408)
IEEE DOI 1812
cameras, feature extraction, image matching, image reconstruction, learning (artificial intelligence), image reconstruction, driving BibRef

Li, Y., Zhang, J., Cheng, Y., Huang, K., Tan, T.,
Semantics-guided multi-level RGB-D feature fusion for indoor semantic segmentation,
ICIP17(1262-1266)
IEEE DOI 1803
Feature extraction, Fuses, Image segmentation, Legged locomotion, Semantics, Streaming media, Sun, Indoor semantic segmentation, RGB-D BibRef

Liu, F., Li, S., Zhang, L., Zhou, C., Ye, R., Wang, Y., Lu, J.,
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-Scale 3D Point Clouds,
ICCV17(5679-5688)
IEEE DOI 1802
convolution, feature extraction, grammars, image classification, image segmentation, BibRef

Namin, S.R., Alvarez, J.M., Petersson, L.,
2D-3D semantic segmentation using cardinality as higher-order loss,
ICPR16(3775-3780)
IEEE DOI 1705
Image edge detection, Image segmentation, Labeling, Sensors, Training. BibRef

Wang, J.H.[Jing-Hua], Wang, Z.H.[Zhen-Hua], Tao, D.C.[Da-Cheng], See, S.[Simon], Wang, G.[Gang],
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks,
ECCV16(V: 664-679).
Springer DOI 1611
BibRef

Tang, B., Zhou, Y., Yu, Y., Du, S.,
Higher-order class-specific priors for semantic segmentation of 3D outdoor scenes,
WACV16(1-9)
IEEE DOI 1606
Analytical models BibRef

Savinov, N.[Nikolay], Ladicky, L.[Lubor], Hane, C.[Christian], Pollefeys, M.[Marc],
Discrete optimization of ray potentials for semantic 3D reconstruction,
CVPR15(5511-5518)
IEEE DOI 1510
BibRef

Fooladgar, F., Kasaei, S.,
Semantic Segmentation of RGB-D Images Using 3D and Local Neighbouring Features,
DICTA15(1-7)
IEEE DOI 1603
computer vision BibRef

Banica, D.[Dan], Sminchisescu, C.[Cristian],
Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images,
CVPR15(3517-3526)
IEEE DOI 1510
BibRef

Samrouth, K., Deforges, O., Liu, Y.[Yi], Falou, W., Khalil, M.,
A joint 3D image semantic segmentation and scalable coding scheme with ROI approach,
VCIP14(270-273)
IEEE DOI 1504
data compression BibRef

Namin, S.T.[Sarah Taghavi], Najafi, M.[Mohammad], Salzmann, M.[Mathieu], Petersson, L.[Lars],
A Multi-modal Graphical Model for Scene Analysis,
WACV15(1006-1013)
IEEE DOI 1503
Graphical models 2D-3D data. Semantic segmentation. BibRef

Kundu, A.[Abhijit], Li, Y.[Yin], Dellaert, F.[Frank], Li, F.X.[Fu-Xin], Rehg, J.M.[James M.],
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video,
ECCV14(VI: 703-718).
Springer DOI 1408
BibRef

Lin, D.[Dahua], Fidler, S.[Sanja], Urtasun, R.[Raquel],
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras,
ICCV13(1417-1424)
IEEE DOI 1403
BibRef

Floros, G.[Georgios], Leibe, B.[Bastian],
Joint 2D-3D temporally consistent semantic segmentation of street scenes,
CVPR12(2823-2830).
IEEE DOI 1208
BibRef

Micusik, B.[Branislav], Kosecka, J.[Jana],
Semantic segmentation of street scenes by superpixel co-occurrence and 3D geometry,
ObjectEvent09(625-632).
IEEE DOI 0910
identify as one of a few common object/background classes. BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
The Facet Model for Descriptions .


Last update:Nov 1, 2021 at 09:26:50