CAM3DNet outperforms prior camera-based 3D detectors on nuScenes, Waymo and Argoverse by using three new modules to better mine multi-scale spatiotemporal features from 2D queries and pyramid maps.
In arXiv preprint arXiv:2301.07870
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Fast-BEV++ achieves at least 3x speedup over Fast-BEV, a new SOTA of 0.488 NDS on nuScenes 3D detection, and over 134 FPS inference by redesigning the core transformation pipeline and adding a learnable depth module.
citing papers explorer
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CAM3DNet: Comprehensively mining the multi-scale features for 3D Object Detection with Multi-View Cameras
CAM3DNet outperforms prior camera-based 3D detectors on nuScenes, Waymo and Argoverse by using three new modules to better mine multi-scale spatiotemporal features from 2D queries and pyramid maps.
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Fast-BEV++: Fast by Algorithm, Deployable by Design
Fast-BEV++ achieves at least 3x speedup over Fast-BEV, a new SOTA of 0.488 NDS on nuScenes 3D detection, and over 134 FPS inference by redesigning the core transformation pipeline and adding a learnable depth module.