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arXiv:2203. 05625v3 [cs. CV] 19 Jul 2022 Experiments show that PETR achieves state-of-the-art performance (50 4% NDS and 44 1% mAP) on standard nuScenes dataset and ranks 1st place on 3D object detection leaderboard
megvii-research PETR | DeepWiki PETR (Position Embedding TRansformer) is a framework for 3D perception from multi-camera images, specifically designed for 3D object detection and Bird's Eye View (BEV) segmentation tasks
PETR: Position Embedding Transformation for Multi-view 3D Object . . . In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features
PETR README. md at main · megvii-research PETR · GitHub PETR develops position embedding transformation (PETR) for multi-view 3D object detection PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imag PETRv2, a unified frame-work for 3D perception from multi-view images Based on PETR [24], PETRv2 explores the effectiveness of temporal modeling, which utilizes th temporal information of pre-vious frames to boost 3D object detection More specif-ically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling The 3D PE
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which utilizes the temporal information of previous frames to boost 3D object detection More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling