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Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection.
Über die stetige abbildung einer linie auf ein flächenstück
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Chenhang He, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang · 2020
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Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
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Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
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Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, and Jiaya Jia · 2023
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Voxelnext: Fully sparse voxelnet for 3d object detection and tracking
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Center-based 3d object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl · 2021
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Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, and Chiew-Lan Tai · 2022
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Spconv: Spatially sparse convolution library
Spconv Contributors · 2022
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Link: Linear kernel for lidar-based 3d perception
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Lest: Large-scale lidar semantic segmentation with transformer
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Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection
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Octformer: Octree-based transformers for 3d point clouds
Peng-Shuai Wang · 2023
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Point transformer v3: Simpler, faster, stronger
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Conquer: Query contrast voxel-detr for 3d object detection
Benjin Zhu, Zhe Wang, Shaoshuai Shi, Hang Xu, Lanqing Hong, and Hongsheng Li · 2023
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