GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion
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VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.
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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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
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VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
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BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.
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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.