fundamentalvision/BEVFormer
[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation. observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1617
- days_rel: n/a
- days_push: 748
- n_releases_24m: 0
Adoption not part of the score
4579 stars · 750 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
BEVFormer is the official PyTorch implementation of an ECCV 2022 paper that learns bird's-eye-view (BEV) representations from multi-camera images using spatiotemporal transformers. It supports camera-only autonomous driving perception tasks such as 3D object detection and map segmentation, achieving state-of-the-art results on nuScenes.
Use cases
- run 3d object detection from camera images on nuScenes
- learn bird's eye view representation from multi-camera images
- segment driving maps from surround-view cameras
- benchmark camera-only perception against lidar baselines
- build a baseline for autonomous driving perception research
- estimate object velocity from cameras without lidar
When to choose
- you need a proven camera-only 3D detection baseline for autonomous driving research
- you want to reproduce or extend a well-cited BEV perception method on nuScenes
- your perception stack must work without lidar, using only surround cameras
When to avoid
- you need a production-ready, actively maintained perception stack for a deployed vehicle
- you lack multi-GPU hardware, since training requires significant GPU memory
- you need lidar or radar fusion rather than camera-only perception
Facets
library · maturity maintenance
deep-learning computer-vision image-processing autonomous-vehicles computer-vision deep-learning machine-learning python 3d-object-detection bird-eye-view nuscenes transformer multi-camera-perception semantic-segmentation research-code eccv-2022 gpu linux
6 sources
- readme: https://github.com/fundamentalvision/BEVFormer · fetched 2026-08-28 · 151921c501e4
- homepage: https://arxiv.org/abs/2203.17270 · fetched 2026-08-29 · ced45b9d92b0
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| fundamentalvision/BEVFormer | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="fundamentalvision/BEVFormer")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem