{"type":"ScholarlyArticle","title":"Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion","authors":[{"name":"Zhaoxin Fan","slug":"zhaoxin-fan","given":"Zhaoxin","family":"Fan","affiliation":"Renmin University of China"},{"name":"Yulin He","slug":"yulin-he","given":"Yulin","family":"He","affiliation":"Renmin University of China"},{"name":"Zhicheng Wang","slug":"zhicheng-wang","given":"Zhicheng","family":"Wang","affiliation":"Nreal"},{"name":"Kejian Wu","slug":"kejian-wu","given":"Kejian","family":"Wu","affiliation":"Nreal"},{"name":"Hongyan Liu","slug":"hongyan-liu","given":"Hongyan","family":"Liu","affiliation":"Tsinghua University"},{"name":"Jun He","slug":"jun-he","given":"Jun","family":"He","affiliation":"Renmin University of China"}],"abstract":"Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.","doi":"10.24963/ijcai.2023/81","url":"https://ijcai.org/proceedings/2023/81","pdf":"https://ijcai.org/proceedings/2023/0081.pdf","markdown":"https://ijcai.org/proceedings/2023/81.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/81","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":81,"pages":{"first":726,"last":735},"keywords":["Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Berthelot et al., 2019a] David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel. Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring. arXiv preprint arXiv:1911.09785, 2019.","title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring","authors":"Berthelot","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Berthelot et al., 2019b] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi- supervised learning. Advances in Neural Information Pro- cessing Systems, 32, 2019.","title":"Mixmatch: A holistic approach to semisupervised learning","authors":"Berthelot","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2022] Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, and Hongsheng Li. Learn- ing a structured latent space for unsupervised point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5543- 5553, 2022.","title":"Learning a structured latent space for unsupervised point cloud completion","authors":"Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, Hongsheng Li","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chang et al., 2015] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.","title":"Shapenet: An information-rich 3d model repository","authors":"Thomas Angel X Chang, Leonidas Funkhouser, Pat Guibas, Qixing Hanrahan, Zimo Huang, Silvio Li, Manolis Savarese, Shuran Savva, Hao Song, Su","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chapelle et al., 2009] Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]. IEEE Transactions on Neural Networks, 20(3):542-542, 2009.","title":"Semi-supervised learning","authors":"Olivier Chapelle, Bernhard Scholkopf, Alexander Zien","year":"2006","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2019] Xuelin Chen, Baoquan Chen, and Niloy J Mitra. Unpaired point cloud completion on real scans us- ing adversarial training. arXiv preprint arXiv:1904.00069, 2019.","title":"Unpaired point cloud completion on real scans using adversarial training","authors":"Chen","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2020] Peibin Chen, Tao Ma, Xu Qin, Weidi Xu, and Shuchang Zhou. Data-efficient semi-supervised learning by reliable edge mining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9192-9201, 2020.","title":"Data-efficient semi-supervised learning by reliable edge mining","authors":"Chen","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cheng et al., 2021] Mingmei Cheng, Le Hui, Jin Xie, and Jian Yang. Sspc-net: Semi-supervised semantic 3d point cloud segmentation network. arXiv preprint arXiv:2104.07861, 2021.","title":"Sspc-net: Semi-supervised semantic 3d point cloud segmentation network","authors":"Mingmei Cheng, Le Hui, Jin Xie, Jian Yang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dai et al., 2017] Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Russ R Salakhutdinov. Good semi- supervised learning that requires a bad gan. Advances in neural information processing systems, 30, 2017.","title":"Good semisupervised learning that requires a bad gan","authors":"Dai","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Denton et al., 2016] Emily Denton, Sam Gross, and Rob Fergus. Semi-supervised learning with context- conditional generative adversarial networks. arXiv preprint arXiv:1611.06430, 2016.","title":"Semi-supervised learning with contextconditional generative adversarial networks","authors":"Emily Denton, Sam Gross, Rob Fergus","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Geiger et al., 2013] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Re- search, 32(11):1231-1237, 2013.","title":"Vision meets robotics: The kitti dataset","authors":"Andreas Geiger, Philip Lenz, Christoph Stiller, Raquel Urtasun","year":"2013","venue":"The International Journal of Robotics Research","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Grandvalet and Bengio, 2004] Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimiza- tion. Advances in neural information processing systems, 17, 2004.","title":"Semi-supervised learning by entropy minimization","authors":"Bengio Grandvalet, Yves Grandvalet, Yoshua Bengio","year":"2004","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2020a] Xiaoshui Huang, Guofeng Mei, and Jian Zhang. Feature-metric registration: A fast semi- supervised approach for robust point cloud registra- tion without correspondences. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11366-11374, 2020.","title":"Feature-metric registration: A fast semisupervised approach for robust point cloud registration without correspondences","authors":"Huang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2020b] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. arXiv preprint arXiv:2003.00410, 2020.","title":"Pf-net: Point fractal network for 3d point cloud completion","authors":"Huang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Iscen et al., 2019] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5070-5079, 2019.","title":"Label propagation for deep semi-supervised learning","authors":"Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lee and others, 2013] Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, volume 3, page 896, 2013.","title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","authors":"Dong-Hyun Lee","year":"2013","venue":"Workshop on challenges in representation learning, ICML","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019] Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7203-7212, 2019.","title":"Pu-gan: a point cloud upsampling adversarial network","authors":"Li","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2020] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and Shi-Min Hu. Morphing and sampling net- work for dense point cloud completion. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 11596-11603, 2020.","title":"Morphing and sampling network for dense point cloud completion","authors":"Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ouali et al., 2020] Yassine Ouali, Céline Hudelot, and Myr- iam Tami. Semi-supervised semantic segmentation with cross-consistency training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12674-12684, 2020.","title":"Semi-supervised semantic segmentation with cross-consistency training","authors":"Yassine Ouali, Céline Hudelot, Myriam Tami","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pan et al., 2021] Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang, Haiyu Zhao, Shuai Yi, and Ziwei Liu. Vari- ational relational point completion network. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8524-8533, 2021.","title":"Variational relational point completion network","authors":"Xinyi Liang Pan, Zhongang Chen, Junzhe Cai, Haiyu Zhang, Shuai Zhao, Ziwei Yi, Liu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Pan, 2020] Liang Pan. Ecg: Edge-aware point cloud com- pletion with graph convolution. IEEE Robotics and Au- tomation Letters, 5(3):4392-4398, 2020.","title":"Ecg: Edge-aware point cloud completion with graph convolution","authors":"Liang Pan","year":"2020","venue":"IEEE Robotics and Automation Letters","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Qi et al., 2017] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","authors":"Hao Charles R Qi, Kaichun Su, Leonidas J Mo, Guibas","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Rasmus et al., 2015] Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko. Semi- supervised learning with ladder networks. Advances in neural information processing systems, 28, 2015.","title":"Semisupervised learning with ladder networks","authors":"Rasmus","year":"2015","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Sajjadi et al., 2016] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization with stochastic trans- formations and perturbations for deep semi-supervised learning. Advances in neural information processing sys- tems, 29, 2016.","title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","authors":"Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sarmad et al., 2019] Muhammad Sarmad, Hyunjoo Jenny Lee, and Young Min Kim. Rl-gan-net: A reinforcement learning agent controlled gan network for real-time point cloud shape completion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5898-5907, 2019.","title":"Rl-gan-net: A reinforcement learning agent controlled gan network for real-time point cloud shape completion","authors":"Sarmad","year":"2019","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tang et al., 2016] Yuxing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandréa, Robert Gaizauskas, and Liming Chen. Large scale semi-supervised object detection using visual and semantic knowledge transfer. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2119-2128, 2016.","title":"Large scale semi-supervised object detection using visual and semantic knowledge transfer","authors":"Tang","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tchapmi et al., 2019] Lyne P Tchapmi, Vineet Kosaraju, Hamid Rezatofighi, Ian Reid, and Silvio Savarese. Top- net: Structural point cloud decoder. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 383-392, 2019.","title":"Topnet: Structural point cloud decoder","authors":"Vineet Lyne P Tchapmi, Hamid Kosaraju, Ian Rezatofighi, Silvio Reid, Savarese","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2020a] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020.","title":"Cascaded refinement network for point cloud completion","authors":"Wang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2020b] Yida Wang, David Joseph Tan, Nas- sir Navab, and Federico Tombari. Softpoolnet: Shape descriptor for point cloud completion and classification. arXiv preprint arXiv:2008.07358, 2020.","title":"Softpoolnet: Shape descriptor for point cloud completion and classification","authors":"Wang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2022] Yida Wang, David Joseph Tan, Nassir Navab, and Federico Tombari. Softpool++: An encoder- decoder network for point cloud completion. International Journal of Computer Vision, 130(5):1145-1164, 2022.","title":"Softpool++: An encoderdecoder network for point cloud completion","authors":"Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari","year":"2022","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xia et al., 2021] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. arXiv preprint arXiv:2101.05278, 2021.","title":"Gan inversion: A survey","authors":"Xia","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xie et al., 2020] Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun. Grnet: Gridding residual network for dense point cloud completion. In European Conference on Computer Vision, pages 365-381. Springer, 2020.","title":"Grnet: Gridding residual network for dense point cloud completion","authors":"Xie","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yan et al., 2022] Xuejun Yan, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu, and Li Lu. Fbnet: Feedback network for point cloud completion. In Computer Vision-ECCV 2022: 17th European Confer- ence, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II, pages 676-693. Springer, 2022.","title":"Fbnet: Feedback network for point cloud completion","authors":"Xuejun Yan, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu, Li Lu","year":"2022","venue":"Computer Vision-ECCV 2022: 17th European Conference","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yang et al., 2018] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 206-215, 2018.","title":"Foldingnet: Point cloud auto-encoder via deep grid deformation","authors":"Yang","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yang et al., 2021] Xiangli Yang, Zixing Song, Irwin King, and Zenglin Xu. A survey on deep semi-supervised learn- ing. arXiv preprint arXiv:2103.00550, 2021.","title":"A survey on deep semi-supervised learning","authors":"Yang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yu et al., 2023] Xumin Yu, Yongming Rao, Ziyi Wang, Ji- wen Lu, and Jie Zhou. Adapointr: Diverse point cloud completion with adaptive geometry-aware transformers. arXiv preprint arXiv:2301.04545, 2023.","title":"Adapointr: Diverse point cloud completion with adaptive geometry-aware transformers","authors":"Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yuan et al., 2018] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point com- pletion network. In 2018 International Conference on 3D Vision (3DV), pages 728-737. IEEE, 2018.","title":"Pcn: Point completion network","authors":"Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert","year":"2018","venue":"International Conference on 3D Vision (3DV)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2020] Wenxiao Zhang, Qingan Yan, and Chunxia Xiao. Detail preserved point cloud comple- tion via separated feature aggregation. arXiv preprint arXiv:2007.02374, 2020.","title":"Detail preserved point cloud completion via separated feature aggregation","authors":"Zhang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2021] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through gan inversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1768-1777, 2021.","title":"Unsupervised 3d shape completion through gan inversion","authors":"Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhao et al., 2020] Na Zhao, Tat-Seng Chua, and Gim Hee Lee. Sess: Self-ensembling semi-supervised 3d object de- tection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11079- 11087, 2020.","title":"Sess: Self-ensembling semi-supervised 3d object detection","authors":"Na Zhao, Tat-Seng Chua, Gim Hee, Lee","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhou et al., 2021] Zhongkai Zhou, Xinnan Fan, Pengfei Shi, and Yuanxue Xin. R-msfm: Recurrent multi-scale feature modulation for monocular depth estimating. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12777-12786, 2021.","title":"R-msfm: Recurrent multi-scale feature modulation for monocular depth estimating","authors":"Zhou","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhu et al., 2020] Xinge Zhu, Yuexin Ma, Tai Wang, Yan Xu, Jianping Shi, and Dahua Lin. Ssn: signature networks for multi-class object detection from point clouds. In Eu- ropean Conference on Computer Vision, pages 581-597. Springer, 2020.","title":"Ssn: signature networks for multi-class object detection from point clouds","authors":"Zhu","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhu, 2005] Xiaojin Jerry Zhu. Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison Department of Computer Sciences, 2005.","title":"Semi-supervised learning literature survey","authors":"Jerry Xiaojin, Zhu","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}