{"type":"ScholarlyArticle","title":"Null-Space Diffusion Sampling for Zero-Shot Point Cloud Completion","authors":[{"name":"Xinhua Cheng","slug":"xinhua-cheng","given":"Xinhua","family":"Cheng","affiliation":"Shenzhen Graduate School , Peking University , China"},{"name":"Nan Zhang","slug":"nan-zhang","given":"Nan","family":"Zhang","affiliation":"Shenzhen Graduate School , Peking University , China"},{"name":"Jiwen Yu","slug":"jiwen-yu","given":"Jiwen","family":"Yu","affiliation":"Shenzhen Graduate School , Peking University , China"},{"name":"Yinhuai Wang","slug":"yinhuai-wang","given":"Yinhuai","family":"Wang","affiliation":"Shenzhen Graduate School , Peking University , China"},{"name":"Ge Li","slug":"ge-li","given":"Ge","family":"Li","affiliation":""},{"name":"Jian Zhang","slug":"jian-zhang","given":"Jian","family":"Zhang","affiliation":"Shenzhen Graduate School , Peking University , China"}],"abstract":"Point cloud completion aims at estimating the complete data of objects from degraded observations. Despite existing completion methods achieving impressive performances, they rely heavily on degraded-complete data pairs for supervision. In this work, we propose a novel framework named Null-Space Diffusion Sampling (NSDS) to solve the point cloud completion task in a zero-shot manner.  By leveraging a pre-trained point cloud diffusion model as the off-the-shelf generator, our sampling approach can generate desired completion outputs with the guidance of the observed degraded data without any extra training. Furthermore, we propose a tolerant loop mechanism to improve the quality of completion results for hard cases. Experimental results demonstrate our zero-shot framework achieves superior completion performance than unsupervised methods and comparable performance to supervised methods in various degraded situations.","doi":"10.24963/ijcai.2023/69","url":"https://ijcai.org/proceedings/2023/69","pdf":"https://ijcai.org/proceedings/2023/0069.pdf","markdown":"https://ijcai.org/proceedings/2023/69.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/69","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":69,"pages":{"first":618,"last":626},"keywords":["Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"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 (CVPR), pages 5543-5553, June 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 (CVPR)","doi":null,"url":null,"internal_url":null},{"position":2,"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":3,"raw":"[Chang et al., 2017] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niebner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Mat- terport3d: Learning from rgb-d data in indoor environ- ments. In Proceedings of the International Conference on 3D Vision (3DV), pages 667-676. IEEE, 2017.","title":"Matterport3d: Learning from rgb-d data in indoor environments","authors":"Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niebner, Manolis Savva, Shuran Song, Andy Zeng, Yinda Zhang","year":"2017","venue":"Proceedings of the International Conference on 3D Vision (3DV)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen and Davies, 2020] Dongdong Chen and Mike E Davies. Deep decomposition learning for inverse imag- ing problems. In Proceedings of the European Conference on Computer Vision (ECCV), pages 510-526, 2020.","title":"Deep decomposition learning for inverse imaging problems","authors":"Chen, Davies ; ] Dongdong Chen, Mike E Davies","year":"2020","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2020] Xuelin Chen, Baoquan Chen, and Niloy J Mitra. Unpaired point cloud completion on real scans us- ing adversarial training. In Proceedings of the Interna- tional Conference on Learning Representations (ICLR), 2020.","title":"Unpaired point cloud completion on real scans using adversarial training","authors":"Chen","year":"2020","venue":"Proceedings of the International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dhariwal and Nichol, 2021] Prafulla Dhariwal and Alexan- der Nichol. Diffusion models beat gans on image synthe- sis. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), volume 34, pages 8780- 8794, 2021.","title":"Diffusion models beat gans on image synthesis","authors":"Nichol ; Dhariwal, Nichol","year":"2021","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR), 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":"Proceedings of the International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Geiger et al., 2012] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), 2012.","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","authors":"Andreas Geiger, Philip Lenz, Raquel Urtasun","year":"2012","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 6840-6851, 2020.","title":"Denoising diffusion probabilistic models","authors":"Jonathan Ho, Ajay Jain, Pieter Abbeel","year":"2020","venue":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2020] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.","title":"Pf-net: Point fractal network for 3d point cloud completion","authors":"Huang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":11,"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 (AAAI), 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 (AAAI)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2021] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV), pages 10012-10022, October 2021.","title":"Swin transformer: Hierarchical vision transformer using shifted windows","authors":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lugmayr et al., 2022] Andreas Lugmayr, Martin Danell- jan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11461-11471, 2022.","title":"Repaint: Inpainting using denoising diffusion probabilistic models","authors":"Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Luo and Hu, 2021] Shitong Luo and Wei Hu. Diffusion probabilistic models for 3d point cloud generation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2837-2845, 2021.","title":"Diffusion probabilistic models for 3d point cloud generation","authors":"Hu ; ] Shitong Luo, Wei Luo, Hu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lyu et al., 2022] Zhaoyang Lyu, Zhifeng Kong, XU Xudong, Liang Pan, and Dahua Lin. A condi- tional point diffusion-refinement paradigm for 3d point cloud completion. In Proceedings of the International Conference on Learning Representations (ICLR), 2022.","title":"A conditional point diffusion-refinement paradigm for 3d point cloud completion","authors":"Zhaoyang Lyu, Zhifeng Kong, Liang Xu Xudong, Dahua Pan, Lin","year":"2022","venue":"Proceedings of the International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":16,"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 (CVPR), pages 8524-8533, June 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 (CVPR)","doi":null,"url":null,"internal_url":null},{"position":17,"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":18,"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 (CVPR), June 2019.","title":"Topnet: Structural point cloud decoder","authors":"P Lyne, Vineet Tchapmi, Hamid Kosaraju, Ian Rezatofighi, Silvio Reid, Savarese","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2020] 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 (CVPR), June 2020.","title":"Cascaded refinement network for point cloud completion","authors":"Xiaogang Wang, Marcelo H Ang, Hee Gim, Lee","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2022] Yida Wang, David Joseph Tan, Nassir Navab, and Federico Tombari. Learning local displace- ments for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1568-1577, June 2022.","title":"Learning local displacements for point cloud completion","authors":"Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2023a] Yinhuai Wang, Yujie Hu, Jiwen Yu, and Jian Zhang. Gan prior based null-space learning for con- sistent super-resolution. In Proceedings of the AAAI con- ference on artificial intelligence (AAAI), 2023.","title":"Gan prior based null-space learning for consistent super-resolution","authors":"Wang","year":"2023","venue":"Proceedings of the AAAI conference on artificial intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2023b] Yinhuai Wang, Jiwen Yu, Runyi Yu, and Jian Zhang. Unlimited-size diffusion restoration. arXiv preprint arXiv:2303.00354, 2023.","title":"Unlimited-size diffusion restoration","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2023c] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffu- sion null-space model. In Proceedings of the International Conference on Learning Representations (ICLR), 2023.","title":"Zero-shot image restoration using denoising diffusion null-space model","authors":"Wang","year":"2023","venue":"Proceedings of the International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wen et al., 2020] Xin Wen, Tianyang Li, Zhizhong Han, and Yu-Shen Liu. Point cloud completion by skip- attention network with hierarchical folding. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.","title":"Point cloud completion by skipattention network with hierarchical folding","authors":"Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wen et al., 2021] Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Yu-Shen Liu. Cy- cle4completion: Unpaired point cloud completion using cycle transformation with missing region coding. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13080-13089, June 2021.","title":"Cycle4completion: Unpaired point cloud completion using cycle transformation with missing region coding","authors":"Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wu et al., 2020] Rundi Wu, Xuelin Chen, Yixin Zhuang, and Baoquan Chen. Multimodal shape completion via conditional generative adversarial networks. In Proceed- ings of the European Conference on Computer Vision (ECCV), pages 281-296. Springer, 2020.","title":"Multimodal shape completion via conditional generative adversarial networks","authors":"Rundi Wu, Xuelin Chen, Yixin Zhuang, Baoquan Chen","year":"2020","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Xia et al., 2021] Yaqi Xia, Yan Xia, Wei Li, Rui Song, Kailang Cao, and Uwe Stilla. Asfm-net: Asymmetrical siamese feature matching network for point completion. In Proceedings of the 29th ACM International Conference on Multimedia, pages 1938-1947, 2021.","title":"Asfm-net: Asymmetrical siamese feature matching network for point completion","authors":"Yaqi Xia, Yan Xia, Wei Li, Rui Song, Kailang Cao, Uwe Stilla","year":"2021","venue":"Proceedings of the 29th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xiang et al., 2021] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5499-5509, October 2021.","title":"Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer","authors":"Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Zhizhong Han","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":29,"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 Proceedings of the European Conference on Computer Vision (ECCV), pages 365-381. Springer, 2020.","title":"Grnet: Gridding residual network for dense point cloud completion","authors":"Xie","year":"2020","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yu et al., 2021] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12498-12507, October 2021.","title":"Pointr: Diverse point cloud completion with geometry-aware transformers","authors":"Yu","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yu et al., 2023] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. Freedom: Training-free energy-guided conditional diffusion model. arXiv preprint arXiv:2303.09833, 2023.","title":"Freedom: Training-free energy-guided conditional diffusion model","authors":"Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, Jian Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yuan et al., 2018] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point com- pletion network. In Proceedings of the International Con- ference 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":"Proceedings of the International Conference on 3D Vision (3DV)","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zeng et al., 2022] Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. Lion: Latent point diffusion models for 3d shape generation. arXiv preprint arXiv:2210.06978, 2022.","title":"Lion: Latent point diffusion models for 3d shape generation","authors":"Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhang et al., 2018] Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, and Ji- ajun Wu. Learning to reconstruct shapes from unseen classes. Proceedings of the Advances in neural informa- tion processing systems (NeurIPS), 31, 2018.","title":"Learning to reconstruct shapes from unseen classes","authors":"Zhang","year":"2018","venue":"Proceedings of the Advances in neural information processing systems (NeurIPS)","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang et al., 2020] Wenxiao Zhang, Qingan Yan, and Chunxia Xiao. Detail preserved point cloud completion via separated feature aggregation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 512-528. Springer, 2020.","title":"Detail preserved point cloud completion via separated feature aggregation","authors":"Zhang","year":"2020","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":36,"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 (CVPR), pages 1768-1777, June 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 (CVPR)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhou et al., 2021] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel dif- fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5826- 5835, 2021.","title":"3d shape generation and completion through point-voxel diffusion","authors":"Zhou","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}