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Training deep models for semantic scene completion (SSC) is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label noise for moving objects.
Distilling the Knowledge in a Neural Network
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Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser · 2017
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SemanticKITTI: A Dataset for Semantic Scene Understanding of Lidar Sequences
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Two Stream 3D Semantic Scene Completion
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Yuenan Hou, Zheng Ma, Chunxiao Liu, and Chen Change Loy · 2019
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Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui · 2021
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Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Xuemeng Yang, Hao Zou, Xin Kong, Tianxin Huang, Yong Liu, Wanlong Li, Feng Wen, and Hongbo Zhang · 2021
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Cylindrical and Asymmetrical 3D Convolution Networks for Lidar-based Perception
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Wei Li, Yuexin Ma, Hongsheng Li, Ruigang Yang, and Dahua Lin · 2021
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Cylindrical and Asymmetrical 3D Convolution Networks for Lidar Segmentation
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin · 2021
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Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
Hao Zou, Xuemeng Yang, Tianxin Huang, Chujuan Zhang, Yong Liu, Wanlong Li, Feng Wen, and Hongbo Zhang · 2021
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Categorical Relation-preserving Contrastive Knowledge Distillation for Medical Image Classification
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3d Semantic Scene Completion: A Survey
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MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
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