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Point cloud shape completion is a challenging problem in 3D vision and robotics.
Sorkine, O., Cohen-Or, D.: Least-squares meshes. In: Proceedings Shape Modeling Applications,. pp. 191–199. IEEE (2004)
2004
Earlier work this paper cites.
Thrun, S., Wegbreit, B.: Shape from symmetry. In: ICCV. vol. 2, pp. 1824–1831. IEEE (2005)
2005
Earlier work this paper cites.
Mitra, N.J., Guibas, L.J., Pauly, M.: Partial and approximate symmetry detection for 3d geometry. In: TOG. vol. 25, pp. 560–568. ACM (2006)
2006
Earlier work this paper cites.
Nealen, A., Igarashi, T., Sorkine, O., Alexa, M.: Laplacian mesh optimization. In: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia. pp. 381–389. ACM (2006)
2006
Earlier work this paper cites.
Pauly, M., Mitra, N.J., Wallner, J., Pottmann, H., Guibas, L.J.: Discovering structural regularity in 3d geometry. In: TOG. vol. 27, p. 43. ACM (2008)
2008
Earlier work this paper cites.
Kim, Y.M., Mitra, N.J., Yan, D.M., Guibas, L.: Acquiring 3d indoor environments with variability and repetition. TOG 31
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. The International Journal of Robotics Research 32
2013
Earlier work this paper cites.
Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ToG 32
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
Li, Y., Dai, A., Guibas, L., Nießner, M.: Database-assisted object retrieval for real-time 3d reconstruction. In: CGF. vol. 34, pp. 435–446. Wiley Online Library (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: CVPR. pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Li, D., Shao, T., Wu, H., Zhou, K.: Shape completion from a single rgbd image. TVCG 23
2016
Earlier work this paper cites.
Shi, Y., Long, P., Xu, K., Huang, H., Xiong, Y.: Data-driven contextual modeling for 3d scene understanding. Computers & Graphics 55
2016
Earlier work this paper cites.
Dai, A., Ruizhongtai Qi, C., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: CVPR. pp. 5868–5877 (2017)
2017
Earlier work this paper cites.
Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: CVPR. pp. 605–613 (2017)
2017
Earlier work this paper cites.
Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: ICCV. pp. 85–93 (2017)
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. CVPR 1
2017
Cited alongside, same era.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: NeurIPS. pp. 5099–5108 (2017)
2017
Cited alongside, same era.
Varley, J., DeChant, C., Richardson, A., Ruales, J., Allen, P.: Shape completion enabled robotic grasping. In: IROS. pp. 2442–2447. IEEE (2017)
2017
Cited alongside, same era.
Yang, B., Wen, H., Wang, S., Clark, R., Markham, A., Trigoni, N.: 3d object reconstruction from a single depth view with adversarial learning. In: ICCV. pp. 679–688 (2017)
2017
Cited alongside, same era.
Dai, A., Ritchie, D., Bokeloh, M., Reed, S., Sturm, J., Nießner, M.: Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans. In: CVPR. pp. 4578–4587 (2018)
Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: Pcn: Point completion network. In: 3DV. pp. 728–737. IEEE (2018)
2018
Later among the works it cites.
Chen, Y., Liu, S., Shen, X., Jia, J.: Fast point r-cnn. In: ICCV. pp. 9775–9784 (2019)
2019
Later among the works it cites.
Giancola, S., Zarzar, J., Ghanem, B.: Leveraging shape completion for 3d siamese tracking. In: CVPR. pp. 1359–1368 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
He, T., Huang, H., Yi, L., Zhou, Y., Wu, C., Wang, J., Soatto, S.: Geonet: Deep geodesic networks for point cloud analysis. In: CVPR. pp. 6888–6897 (2019)
2019
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2018
Cited alongside, same era.
Dinesh Reddy, N., Vo, M., Narasimhan, S.G.: Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In: CVPR. pp. 1906–1915 (2018)
2018
Cited alongside, same era.
Hua, B.S., Tran, M.K., Yeung, S.K.: Pointwise convolutional neural networks. In: CVPR. pp. 984–993 (2018)
2018
Cited alongside, same era.
Le, T., Duan, Y.: Pointgrid: A deep network for 3d shape understanding. In: CVPR. pp. 9204–9214 (2018)
2018
Cited alongside, same era.
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: CVPR. pp. 918–927 (2018)
2018
Cited alongside, same era.
Rethage, D., Wald, J., Sturm, J., Navab, N., Tombari, F.: Fully-convolutional point networks for large-scale point clouds. In: ECCV. pp. 596–611 (2018)
2018
Cited alongside, same era.
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: Splatnet: Sparse lattice networks for point cloud processing. In: CVPR. pp. 2530–2539 (2018)
2018
Cited alongside, same era.
Wang, C., Samari, B., Siddiqi, K.: Local spectral graph convolution for point set feature learning. In: ECCV. pp. 52–66 (2018)
2018
Cited alongside, same era.
Later among the works it cites.
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. In: CVPR. pp. 12697–12705 (2019)
2019
Later among the works it cites.
Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: CVPR. pp. 8895–8904 (2019)
2019
Later among the works it cites.
Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3d object detection in point clouds. In: ICCV. pp. 9277–9286 (2019)
2019
Later among the works it cites.
Sarmad, M., Lee, H.J., Kim, Y.M.: Rl-gan-net: A reinforcement learning agent controlled gan network for real-time point cloud shape completion. In: CVPR. pp. 5898–5907 (2019)
2019
Later among the works it cites.
Shi, S., Wang, X., Li, H.: Pointrcnn: 3d object proposal generation and detection from point cloud. In: CVPR. pp. 770–779 (2019)
2019
Later among the works it cites.
Tchapmi, L.P., Kosaraju, V., Rezatofighi, H., Reid, I., Savarese, S.: Topnet: Structural point cloud decoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 383–392 (2019)
2019
Later among the works it cites.
Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., Weinberger, K.Q.: Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. In: CVPR. pp. 8445–8453 (2019)
2019
Later among the works it cites.
Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: CVPR. pp. 9621–9630 (2019)
2019
Later among the works it cites.
Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: ICCV. pp. 4541–4550 (2019)
2019
Later among the works it cites.
Zhao, H., Jiang, L., Fu, C.W., Jia, J.: Pointweb: Enhancing local neighborhood features for point cloud processing. In: CVPR. pp. 5565–5573 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.