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- GitHub - megvii-research PETR: [ECCV2022] PETR: Position Embedding . . .
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
- [2203. 05625] PETR: Position Embedding Transformation for Multi-View 3D . . .
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
- 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
- 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
- PETR: Position Embedding Transformation for Multi-View 3D Object . . .
The PETR framework introduces a novel way to integrate 3D spatial information into multi-view 3D object detection By transforming 2D image features into 3D position-aware features, PETR
- PETR: Position Embedding Transformation for Multi-view 3D . . . - Springer
PETR encodes the posi-tion information of 3D coordinates into image features, producing the 3D position-aware features Object query can perceive the 3D position-aware features and perform end-to-end object detection
- 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
- [2206. 01256] PETRv2: A Unified Framework for 3D Perception from Multi . . .
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
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