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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
- All About Petr: The Stickers That Stole Hearts At UC Irvine
Created by an anonymous student in 2018, Petr the Anteatr is the quirky mascot of an entire sticker distribution movement Petr the Anteatr started as an Instagram account (@petr_the_anteatr) that quickly gained a cult following
- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Considering the simplicity of PETR, we wonder if it is possible for the sparse-query paradigm in PETR to follow the success of BEV-based methods In this paper, we aim to build a strong and unified framework by extending the PETR with temporal modeling and the sup-port for multi-task learning
- [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
- 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
- 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
- FQ-PETR: Fully Quantized Position Embedding Transformation for Multi . . .
PETR and its variants (PETRs) excel in benchmarks but face deployment challenges due to high computational cost and memory footprint Quantization is an effective technique for compressing deep neural networks by reducing the bit width of weights and activations
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