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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[ECCV2022] PETR: Position Embedding Transformation for Multi-View 3D Object Detection & [ICCV2023] PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
| Date | Stars |
|---|---|
| 2026-07-24 | 1064 |
| 2026-07-25 | 1064 |
| 2026-07-28 | 1064 |
| 2026-07-30 | 1064 |
| 2026-08-06 | 1064 |
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# [ECCV2022] Position Embedding Transformation for Multi-View 3D Object Detection # [ICCV2023] PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images [](https://arxiv.org/abs/2203.05625)  [](https://paperswithcode.com/sota/3d-object-detection-on-nuscenes-camera-only?p=petrv2-a-unified-framework-for-3d-perception) <!-- ## Introduction --> This repository is an official implementation of [PETR](https://arxiv.org/abs/2203.05625) and [PETRv2](https://arxiv.org/abs/2206.01256). The flash attention version can be find from the "[flash](https://github.com/megvii-research/PETR/tree/flash)" branch. <div align="center"> <img src="figs/overview.png"/> </div><br/> 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. Object query can perceive the 3D position-aware features and perform end-to-end object detection. It can serve as a simple yet strong baseline for future research. <div align="center"> <img src="figs/overall.png"/> </div><br/> PETRv2 is a unified framework for 3D perception from multi-view images. Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which utilizes the temporal information of previous frames to boost 3D object detection. The 3D PE achieves the temporal alignment on object position of different frames. A feature-guided position encoder is further introduced to improve the data adaptability of 3D PE. To support for high-quality BEV segmentation, PETRv2 provides a simply yet effective solution by adding a set of segmentation queries. Each segmentation query is responsible for segmenting one specific patch of BEV map. PETRv2 achieves state-of-the-art performance on 3D object detection and BEV segmentation. ## News **2023.10.11** The 3D lane detection of PETRv2 has been released on [TopoMLP](https://github.com/wudongming97/TopoMLP). It support openlanev2 and won the 1st place in CVPR2023 workshop!. **2023.01.25** Our multi-view 3D detection framework [StreamPETR](https://github.com/exiawsh/StreamPETR) (63.6% NDS and 55.0% mAP)** without TTA and future frames. **2023.01.04** Our multi-modal detection framework [CMT](https://github.com/junjie18/CMT) is released on [arxiv](https://arxiv.org/pdf/2301.01283.pdf). **2022.11.04** The code of multi-scale improvement in PETRv2 is released. **2022.09.21** The code of query denoise improvement in PETRv2 is released. **2022.09.04** PETRv2 with VoVNet backbone and multi-scale achieves **(59.1% NDS and 50.8% mAP)**. **2022.08.11** PETRv2 with GLOM-like backbone and query denoise achieves **(59.2% NDS and 51.2% mAP)** without extra data. **2022.07.04** PETR has been accepted by ECCV 2022. **2022.06.28** The code of BEV Segmentation in PETRv2 is released. **2022.06.16** The code of 3D object detection in PETRv2 is released. **2022.06.10** The code of PETR is released. **2022.06.06** PETRv2 is released on [arxiv](https://arxiv.org/abs/2206.01256). **2022.06.01** PETRv2 achieves another SOTA performance on nuScenes dataset **(58.2% NDS and 49.0% mAP)** by the temporal modeling and supports BEV segmentation. **2022.03.10** PETR is released on [arxiv](https://arxiv.org/abs/2203.05625). **2022.03.08** PETR achieves SOTA performance **(50.4% NDS and 44.1% mAP)** on standard nuScenes dataset. ## Preparation This implementation is built upon [detr3d](https://github.com/WangYueFt/detr3d/blob/main/README.md), and can be constructed as the [install.md](./install.md). * Environments Linux, Python==3.6.8, CUDA == 11.2, pytorch == 1.9.0, mmdet3d == 0.1
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:de5ea900f38f21e9, topic:object-detection, desc:object detection, readme:object detection
