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In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems.
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Scalability in perception for autonomous driving: Waymo open dataset
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Fcos: Fully convolutional one-stage object detection
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2019
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Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras
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Deep multi-task learning for joint localization, perception, and prediction
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Safety-aware motion prediction with unseen vehicles for autonomous driving
Xuanchi Ren, Tao Yang, Li Erran Li, Alexandre Alahi, and Qifeng Chen · 2021
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Fcos3d: Fully convolutional one-stage monocular 3d object detection
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Detr3d: 3d object detection from multi-view images via 3d-to-2d queries
Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, , and Justin M. Solomon · 2021
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Center-based 3d object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krähenbühl · 2021
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Objects are different: Flexible monocular 3d object detection
Yunpeng Zhang, Jiwen Lu, and Jie Zhou · 2021
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Stretchbev: Stretching future instance prediction spatially and temporally
Adil Kaan Akan and Fatma Güney · 2022
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Petr: Position embedding transformation for multi-view 3d object detection
Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun · 2022
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Bevsegformer: Bird’s eye view semantic segmentation from arbitrary camera rigs
Lang Peng, Zhirong Chen, Zhangjie Fu, Pengpeng Liang, and Erkang Cheng · 2022
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Probabilistic and geometric depth: Detecting objects in perspective
Tai Wang, ZHU Xinge, Jiangmiao Pang, and Dahua Lin · 2022
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