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In this paper, we address the problem of detecting 3D objects from multi-view images.
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Attention is all you need
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Gs3d: An efficient 3d object detection framework for autonomous driving
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Lasernet: An efficient probabilistic 3d object detector for autonomous driving
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Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
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End-to-end pseudo-lidar for image-based 3d object detection
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Predicting semantic map representations from images using pyramid occupancy networks
Thomas Roddick and Roberto Cipolla · 2020
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Bevstereo: Enhancing depth estimation in multi-view 3d object detection with dynamic temporal stereo
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Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li · 2022
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Is pseudo-lidar needed for monocular 3d object detection?
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Categorical depth distributionnetwork for monocular 3d object detection
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Depth-conditioned dynamic message propagation for monocular 3d object detection
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Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers
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Exploring geometric consistency for monocular 3d object detection
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Dab-detr: Dynamic anchor boxes are better queries for detr
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Learning auxiliary monocular contexts helps monocular 3d object detection
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Probabilistic and geometric depth: Detecting objects in perspective
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Detr3d: 3d object detection from multi-view images via 3d-to-2d queries
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Cross-view transformers for real-time map-view semantic segmentation
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