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Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult.
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Surfconv: Bridging 3d and 2d convolution for rgbd images
Hang Chu, Wei-Chiu Ma3 Kaustav Kundu, Raquel Urtasun, and Sanja Fidler · 2018
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Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Frustum pointnets for 3d object detection from rgb-d data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Monte carlo convolution for learning on non-uniformly sampled point clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski · 2018
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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Qiangui Huang, Weiyue Wang, and Ulrich Neumann · 2018
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Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2018
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Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
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