# OpenDriveLab/Birds-eye-view-Perception

[IEEE T-PAMI 2023] Awesome BEV perception research and cookbook for all level audience in autonomous diriving

Repository: https://github.com/OpenDriveLab/Birds-eye-view-Perception
Canonical: https://ross.abutalabs.com/products/birds-eye-view-perception
Homepage: https://doi.org/10.1109/TPAMI.2023.3333838
Language: Python
License: Apache-2.0
License Family: permissive
Topics: autonomous-driving, birds-eye-view, camera-detection, lidar-detection, perception-algorithm
Last push: 2025-07-21T02:34:06+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 32, release rhythm 8, longevity 100
- inputs: {"age_days": 1474, "days_push": 409, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1381, forks 122 (observed 2026-08-28T04:04:33.920836+00:00)

## What it is
An awesome-list and survey companion for bird's-eye-view (BEV) perception in autonomous driving, paired with an open-source PyTorch BEV toolbox. It curates papers across camera, LiDAR, and fusion modalities and provides recipes for state-of-the-art 3D detection.

## Use cases
- find BEV perception papers for autonomous driving
- learn bird's-eye-view 3D object detection from scratch
- get recipes to improve camera-based 3D detection performance
- compare BEV algorithms for lidar and camera fusion
- find a starting point for BEV perception research
- reproduce state-of-the-art BEV detection results

## When to choose
- you are a researcher or engineer working on BEV perception for autonomous driving
- you want a curated literature survey plus practical training recipes
- you need a PyTorch toolbox baseline for camera-based 3D detection

## When to avoid
- you need a production-ready perception stack for a deployed vehicle
- your task is unrelated to bird's-eye-view or 3D perception
- you want a plug-and-play tool with no ML background

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, computer-vision, benchmarking, documentation
- domain: autonomous-vehicles, computer-vision, deep-learning, tutorials
- platform: python
- tags: birds-eye-view, 3d-object-detection, lidar, camera-perception, awesome-list, survey-paper, autonomous-driving, gpu

## Member repositories
- OpenDriveLab/Birds-eye-view-Perception (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.920836+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:40:09.407274+00:00, confidence not recorded.
  - readme: https://github.com/OpenDriveLab/Birds-eye-view-Perception (fetched 2026-08-28T04:04:33.920836+00:00, sha 3bb6c7ded876)
  - homepage: https://doi.org/10.1109/TPAMI.2023.3333838 (fetched 2026-08-29T11:56:05.386153+00:00, sha 61f461fb3b4d)
- Data as of 2026-08-30T08:39:29.467469+00:00.