{"type":"ScholarlyArticle","title":"Label-efficient Semantic Scene Completion with Scribble Annotations","authors":[{"name":"Song Wang","slug":"song-wang","given":"Song","family":"Wang","affiliation":"Zhejiang University"},{"name":"Jiawei Yu","slug":"jiawei-yu","given":"Jiawei","family":"Yu","affiliation":"Zhejiang University"},{"name":"Wentong Li","slug":"wentong-li","given":"Wentong","family":"Li","affiliation":"Zhejiang University"},{"name":"Hao Shi","slug":"hao-shi","given":"Hao","family":"Shi","affiliation":"Zhejiang University"},{"name":"Kailun Yang","slug":"kailun-yang","given":"Kailun","family":"Yang","affiliation":"Hunan University"},{"name":"Junbo Chen","slug":"junbo-chen","given":"Junbo","family":"Chen","affiliation":"Udeer."},{"name":"Jianke Zhu","slug":"jianke-zhu","given":"Jianke","family":"Zhu","affiliation":"Zhejiang University"}],"abstract":"Semantic scene completion aims to infer the 3D geometric structures with semantic classes from camera or LiDAR, which provide essential occupancy information in autonomous driving. Prior endeavors concentrate on constructing the network or benchmark in a fully supervised manner. While the dense occupancy grids need point-wise semantic annotations, which incur expensive and tedious labeling costs. In this paper, we build a new label-efficient benchmark, named ScribbleSC, where the sparse scribble-based semantic labels are combined with dense geometric labels for semantic scene completion. In particular, we propose a simple yet effective approach called Scribble2Scene, which bridges the gap between the sparse scribble annotations and fully-supervision. Our method consists of geometric-aware auto-labelers construction and online model training with an offline-to-online distillation module to enhance the performance. Experiments on SemanticKITTI demonstrate that Scribble2Scene achieves competitive performance against the fully-supervised counterparts, showing 99% performance of the fully-supervised models with only 13.5% voxels labeled. Both annotations of ScribbleSC and our full implementation are available at https://github.com/songw-zju/Scribble2Scene.","doi":"10.24963/ijcai.2024/155","url":"https://ijcai.org/proceedings/2024/155","pdf":"https://ijcai.org/proceedings/2024/0155.pdf","markdown":"https://ijcai.org/proceedings/2024/155.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/155","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":155,"pages":{"first":1398,"last":1406},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Bearman et al., 2016] Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei. What's the point: Seman- tic segmentation with point supervision. In ECCV, 2016.","title":"What's the point: Semantic segmentation with point supervision","authors":"Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Behley et al., 2019] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. In ICCV, 2019.","title":"SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences","authors":"Behley","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2021] Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, and Hongsheng Li. Se- mantic scene completion via integrating instances and scene in-the-loop. In CVPR, 2021.","title":"Semantic scene completion via integrating instances and scene in-the-loop","authors":"Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, Hongsheng Li","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao and de Charette, 2022] Anh-Quan Cao and Raoul de Charette. MonoScene: Monocular 3D semantic scene completion. In CVPR, 2022.","title":"MonoScene: Monocular 3D semantic scene completion","authors":"Anh-Quan Cao, Raoul De Charette","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2021] Ran Cheng, Christopher Agia, Yuan Ren, Xinhai Li, and Liu Bingbing. S3CNet: A sparse se- mantic scene completion network for LiDAR point cloud. In CoRL, 2021.","title":"S3CNet: A sparse semantic scene completion network for LiDAR point cloud","authors":"Ran Cheng, Christopher Agia, Yuan Ren, Xinhai Li, Liu Bingbing","year":"2021","venue":"CoRL","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chibane et al., 2022] Julian Chibane, Francis Engelmann, Tuan Anh Tran, and Gerard Pons-Moll. Box2Mask: Weakly supervised 3D semantic instance segmentation us- ing bounding boxes. In ECCV, 2022.","title":"Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes","authors":"Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cho and Hariharan, 2019] Jang Hyun Cho and Bharath Har- iharan. On the efficacy of knowledge distillation. In ICCV, 2019.","title":"On the efficacy of knowledge distillation","authors":"Hariharan ; Cho, Hyun Jang, Bharath Cho, Hariharan","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fan et al., 2022] Junsong Fan, Zhaoxiang Zhang, and Tie- niu Tan. Pointly-supervised panoptic segmentation. In ECCV, 2022.","title":"Junsong Fan, Zhaoxiang Zhang, and Tieniu Tan. Pointly-supervised panoptic segmentation","authors":"Fan","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Furlanello et al., 2018] Tommaso Furlanello, Zachary Lip- ton, Michael Tschannen, Laurent Itti, and Anima Anand- kumar. Born again neural networks. In ICML, 2018.","title":"Born again neural networks","authors":"Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Geiger et al., 2012] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012.","title":"Are we ready for autonomous driving? The KITTI vision benchmark suite","authors":"Andreas Geiger, Philip Lenz, Raquel Urtasun","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Heo et al., 2019a] Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi. A comprehensive overhaul of feature distillation. In ICCV, 2019.","title":"A comprehensive overhaul of feature distillation","authors":"Heo","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Heo et al., 2019b] Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi. Knowledge transfer via distil- lation of activation boundaries formed by hidden neurons. In AAAI, 2019.","title":"Knowledge transfer via distillation of activation boundaries formed by hidden neurons","authors":"Heo","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hinton et al., 2015] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.","title":"Distilling the knowledge in a neural network","authors":"Geoffrey Hinton, Oriol Vinyals, Jeff Dean","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2022] Tao Huang, Shan You, Fei Wang, Chen Qian, and Chang Xu. Knowledge distillation from a stronger teacher. In NeurIPS, 2022. Huang et al., 2023] Yuanhui Huang, Wenzhao Zheng, Yun- peng Zhang, Jie Zhou, and Jiwen Lu. Tri-perspective view for vision-based 3D semantic occupancy prediction. In CVPR, 2023.","title":"Tri-perspective view for vision-based 3D semantic occupancy prediction","authors":"Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu, ; Huang","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2020] Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao, and Yue Gao. Attention-based multi-modal fusion network for semantic scene completion. In AAAI, 2020.","title":"Attention-based multi-modal fusion network for semantic scene completion","authors":"Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao, Yue Gao","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2022] Wentong Li, Wenyu Liu, Jianke Zhu, Miao- miao Cui, Xian-Sheng Hua, and Lei Zhang. Box- supervised instance segmentation with level set evolution. In ECCV, 2022.","title":"Boxsupervised instance segmentation with level set evolution","authors":"Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui, Xian-Sheng Hua, Lei Zhang","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2023a] Wentong Li, Yuqian Yuan, Song Wang, Jianke Zhu, Jianshu Li, Jian Liu, and Lei Zhang. Point2mask: Point-supervised panoptic segmentation via optimal transport. In ICCV, 2023.","title":"Point2mask: Point-supervised panoptic segmentation via optimal transport","authors":"Li","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2023b] Yiming Li, Sihang Li, Xinhao Liu, Moon- jun Gong, Kenan Li, Nuo Chen, Zijun Wang, Zhiheng Li, Tao Jiang, Fisher Yu, et al. SSCBench: A large-scale 3d semantic scene completion benchmark for autonomous driving. arXiv preprint arXiv:2306.09001, 2023.","title":"SSCBench: A large-scale 3d semantic scene completion benchmark for autonomous driving","authors":"Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2023c] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. VoxFormer: Sparse voxel trans- former for camera-based 3D semantic scene completion. In CVPR, 2023.","title":"VoxFormer: Sparse voxel transformer for camera-based 3D semantic scene completion","authors":"Li","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2024a] Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui, Risheng Yu Xiansheng Hua, and Lei Zhang. Box2mask: Box-supervised instance segmentation via level-set evolution. T-PAMI, 2024.","title":"Box2mask: Box-supervised instance segmentation via level-set evolution","authors":"Li","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2024b] Wentong Li, Yuqian Yuan, Song Wang, Wenyu Liu, Dongqi Tang, Jianke Zhu, Lei Zhang, et al. Label-efficient segmentation via affinity propagation. In NeurIPS, 2024.","title":"Label-efficient segmentation via affinity propagation","authors":"Li","year":"2024","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liang et al., 2022] Zhiyuan Liang, Tiancai Wang, Xiangyu Zhang, Jian Sun, and Jianbing Shen. Tree energy loss: Towards sparsely annotated semantic segmentation. In CVPR, 2022.","title":"Tree energy loss: Towards sparsely annotated semantic segmentation","authors":"Zhiyuan Liang, Tiancai Wang, Xiangyu Zhang, Jian Sun, Jianbing Shen","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Lin et al., 2016] Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convo- lutional networks for semantic segmentation. In CVPR, 2016.","title":"Scribblesup: Scribble-supervised convolutional networks for semantic segmentation","authors":"Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2018] Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, and Xiaowei Li. See and think: Disentangling semantic scene completion. In NeurIPS, 2018.","title":"See and think: Disentangling semantic scene completion","authors":"Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2022] Yufan Liu, Jiajiong Cao, Bing Li, Weim- ing Hu, and Stephen Maybank. Learning to explore dis- tillability and sparsability: A joint framework for model compression. T-PAMI, 2022.","title":"Learning to explore distillability and sparsability: A joint framework for model compression","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2024] Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, and Jianke Zhu. Mgmap: Mask-guided learning for online vectorized hd map construction. In CVPR, 2024.","title":"Mgmap: Mask-guided learning for online vectorized hd map construction","authors":"Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, Jianke Zhu","year":"2024","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Mei et al., 2023] Jianbiao Mei, Yu Yang, Mengmeng Wang, Tianxin Huang, Xuemeng Yang, and Yong Liu. SSC-RS: Elevate LiDAR semantic scene completion with represen- tation separation and BEV fusion. In IROS, 2023.","title":"SSC-RS: Elevate LiDAR semantic scene completion with representation separation and BEV fusion","authors":"Jianbiao Mei, Yu Yang, Mengmeng Wang, Tianxin Huang, Xuemeng Yang, Yong Liu","year":"2023","venue":"IROS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Pan et al., 2020] Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao. SemanticPOSS: A point cloud dataset with large quantity of dynamic in- stances. In IV, 2020.","title":"SemanticPOSS: A point cloud dataset with large quantity of dynamic instances","authors":"Pan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Roldao et al., 2020] Luis Roldao, Raoul de Charette, and Anne Verroust-Blondet. LMSCNet: Lightweight multi- scale 3D semantic completion. In 3DV, 2020.","title":"LMSCNet: Lightweight multiscale 3D semantic completion","authors":"Luis Roldao, Raoul De Charette, Anne Verroust-Blondet","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Romero et al., 2015] Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. FitNets: Hints for thin deep nets. In ICLR, 2015.","title":"FitNets: Hints for thin deep nets","authors":"Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shi et al., 2024] Hao Shi, Song Wang, Jiaming Zhang, Xi- aoting Yin, Zhongdao Wang, Zhijian Zhao, Guangming Wang, Jianke Zhu, Kailun Yang, and Kaiwei Wang. Oc- cfiner: Offboard occupancy refinement with hybrid propa- gation. arXiv preprint arXiv:2403.08504, 2024.","title":"Occfiner: Offboard occupancy refinement with hybrid propagation","authors":"Hao Shi, Song Wang, Jiaming Zhang, Xiaoting Yin, Zhongdao Wang, Zhijian Zhao, Guangming Wang, Jianke Zhu, Kailun Yang, Kaiwei Wang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sima et al., 2023] Chonghao Sima, Wenwen Tong, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, and Hongyang Li. Scene as occupancy. In ICCV, 2023.","title":"Scene as occupancy","authors":"Chonghao Sima, Wenwen Tong, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, Hongyang Li","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Song et al., 2017] Shuran Song, Fisher Yu, Andy Zeng, An- gel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene completion from a single depth image. In CVPR, 2017.","title":"Semantic scene completion from a single depth image","authors":"Song","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tian et al., 2021] Zhi Tian, Chunhua Shen, Xinlong Wang, and Hao Chen. BoxInst: High-performance instance seg- mentation with box annotations. In CVPR, 2021.","title":"BoxInst: High-performance instance segmentation with box annotations","authors":"Chunhua Zhi Tian, Xinlong Shen, Hao Wang, Chen","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tian et al., 2023] Xiaoyu Tian, Tao Jiang, Longfei Yun, Yue Wang, Yilun Wang, and Hang Zhao. Occ3D: A large-scale 3D occupancy prediction benchmark for autonomous driv- ing. In NeurIPS, 2023.","title":"Occ3D: A large-scale 3D occupancy prediction benchmark for autonomous driving","authors":"Xiaoyu Tian, Tao Jiang, Longfei Yun, Yue Wang, Yilun Wang, Hang Zhao","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Unal et al., 2022] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribble-supervised LiDAR semantic segmen- tation. In CVPR, 2022.","title":"Scribble-supervised LiDAR semantic segmentation","authors":"Ozan Unal, Dengxin Dai, Luc Van Gool","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2022] Song Wang, Jianke Zhu, and Ruixiang Zhang. Meta-rangeseg: Lidar sequence semantic segmen- tation using multiple feature aggregation. RA-L, 2022.","title":"Meta-rangeseg: Lidar sequence semantic segmentation using multiple feature aggregation","authors":"Song Wang, Jianke Zhu, Ruixiang Zhang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2023a] Fengyun Wang, Dong Zhang, Hanwang Zhang, Jinhui Tang, and Qianru Sun. Semantic scene com- pletion with cleaner self. In CVPR, 2023.","title":"Semantic scene completion with cleaner self","authors":"Wang","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2023b] Song Wang, Wentong Li, Wenyu Liu, Xiaolu Liu, and Jianke Zhu. Lidar2map: In defense of lidar-based semantic map construction using online cam- era distillation. In CVPR, 2023.","title":"Lidar2map: In defense of lidar-based semantic map construction using online camera distillation","authors":"Wang","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2023c] Xiaofeng Wang, Zheng Zhu, Wenbo Xu, Yunpeng Zhang, Yi Wei, Xu Chi, Yun Ye, Dalong Du, Jiwen Lu, and Xingang Wang. OpenOccupancy: A large scale benchmark for surrounding semantic occupancy per- ception. In ICCV, 2023.","title":"OpenOccupancy: A large scale benchmark for surrounding semantic occupancy perception","authors":"Wang","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2024] Song Wang, Jiawei Yu, Wentong Li, Wenyu Liu, Xiaolu Liu, Junbo Chen, and Jianke Zhu. Not all voxels are equal: Hardness-aware semantic scene com- pletion with self-distillation. In CVPR, 2024.","title":"Not all voxels are equal: Hardness-aware semantic scene completion with self-distillation","authors":"Song Wang, Jiawei Yu, Wentong Li, Wenyu Liu, Xiaolu Liu, Junbo Chen, Jianke Zhu","year":"2024","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wei et al., 2023] Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, and Jiwen Lu. SurroundOcc: Multi- camera 3D occupancy prediction for autonomous driving. In ICCV, 2023.","title":"SurroundOcc: Multicamera 3D occupancy prediction for autonomous driving","authors":"Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Wilson et al., 2022] Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, and Maani Ghaffari. MotionSC: Data set and network for real-time semantic mapping in dynamic envi- ronments. RA-L, 2022.","title":"MotionSC: Data set and network for real-time semantic mapping in dynamic environments","authors":"Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Xia et al., 2023] Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, and Yu Qiao. SCPNet: Semantic scene completion on point cloud. In CVPR, 2023.","title":"SCPNet: Semantic scene completion on point cloud","authors":"Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yan et al., 2021] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse single sweep LiDAR point cloud segmentation via learn- ing contextual shape priors from scene completion. In AAAI, 2021.","title":"Sparse single sweep LiDAR point cloud segmentation via learning contextual shape priors from scene completion","authors":"Jiantao Xu Yan, Jie Gao, Ruimao Li, Zhen Zhang, Rui Li, Shuguang Huang, Cui","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Yang et al., 2022] Zhendong Yang, Zhe Li, Mingqi Shao, Dachuan Shi, Zehuan Yuan, and Chun Yuan. Masked gen- erative distillation. In ECCV, 2022.","title":"Masked generative distillation","authors":"Zhendong Yang, Zhe Li, Mingqi Shao, Dachuan Shi, Zehuan Yuan, Chun Yuan","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Yao et al., 2023] Jiawei Yao, Chuming Li, Keqiang Sun, Yingjie Cai, Hao Li, Wanli Ouyang, and Hongsheng Li. NDC-Scene: Boost monocular 3D semantic scene com- pletion in normalized device coordinates space. In ICCV, 2023.","title":"NDC-Scene: Boost monocular 3D semantic scene completion in normalized device coordinates space","authors":"Jiawei Yao, Chuming Li, Keqiang Sun, Yingjie Cai, Hao Li, Wanli Ouyang, Hongsheng Li","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Ye and Bors, 2022] Fei Ye and Adrian G Bors. Dynamic self-supervised teacher-student network learning. T-PAMI, 2022.","title":"Dynamic self-supervised teacher-student network learning","authors":"Fei Ye, Adrian G Bors","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Ye and Bors, 2023] Fei Ye and Adrian G Bors. Continual variational autoencoder via continual generative knowl- edge distillation. In AAAI, 2023.","title":"Continual variational autoencoder via continual generative knowledge distillation","authors":"Bors ; ] Fei Ye, Adrian G Ye, Bors","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhang et al., 2018] Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang, and Hongen Liao. Efficient se- mantic scene completion network with spatial group con- volution. In ECCV, 2018.","title":"Efficient semantic scene completion network with spatial group convolution","authors":"Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang, Hongen Liao","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhang et al., 2023] Yunpeng Zhang, Zheng Zhu, and Da- long Du. OccFormer: Dual-path transformer for vision- based 3D semantic occupancy prediction. In ICCV, 2023.","title":"OccFormer: Dual-path transformer for visionbased 3D semantic occupancy prediction","authors":"Yunpeng Zhang, Zheng Zhu, Dalong Du","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zhao et al., 2022] Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang. Decoupled knowledge dis- tillation. In CVPR, 2022.","title":"Decoupled knowledge distillation","authors":"Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, Jiajun Liang","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhu et al., 2021a] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3D convolution networks for lidar segmentation. In CVPR, 2021.","title":"Cylindrical and asymmetrical 3D convolution networks for lidar segmentation","authors":"Zhu","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhu et al., 2021b] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable DETR: Deformable transformers for end-to-end object detection. In ICLR, 2021.","title":"Deformable DETR: Deformable transformers for end-to-end object detection","authors":"Zhu","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}