# nv-tlabs/lift-splat-shoot

Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D (ECCV 2020)

Repository: https://github.com/nv-tlabs/lift-splat-shoot
Canonical: https://ross.abutalabs.com/products/lift-splat-shoot
Language: Python
License: NOASSERTION
License Family: other
Last push: 2024-10-15T01:41:09+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2239, "days_push": 688, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1369, forks 260 (observed 2026-08-28T04:04:31.689177+00:00)

## What it is
PyTorch implementation of Lift-Splat-Shoot (ECCV 2020), an end-to-end model that converts images from arbitrary multi-camera rigs into a bird's-eye-view representation for autonomous vehicle perception. It lifts per-camera image features into 3D frustums, splats them into a BEV grid, and supports tasks like BEV object/map segmentation and interpretable motion planning.

## Use cases
- generate bird's-eye-view segmentation from multi-camera images
- fuse predictions from multiple vehicle cameras into one BEV grid
- train a BEV perception model on nuScenes
- run end-to-end motion planning from camera images
- compare camera-only BEV models against lidar-depth baselines
- research camera-to-BEV unprojection methods

## When to choose
- you need a camera-only bird's-eye-view perception baseline for autonomous driving research
- you work with nuScenes multi-camera data and want a proven ECCV 2020 method
- you want interpretable end-to-end planning via BEV cost maps

## When to avoid
- you need a production-ready, actively maintained perception stack
- you require lidar or radar fusion rather than camera-only input
- you need support for datasets other than nuScenes out of the box

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: autonomous-vehicles, computer-vision, deep-learning, machine-learning
- platform: python
- tags: bird's-eye-view, pytorch, autonomous-driving, nuscenes, multi-camera-fusion, 3d-perception, eccv-2020, research-code

## Member repositories
- nv-tlabs/lift-splat-shoot (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:31.689177+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 2026-08-30T04:41:00.134017+00:00, confidence not recorded.
  - readme: https://github.com/nv-tlabs/lift-splat-shoot (fetched 2026-08-28T04:04:31.689177+00:00, sha d0409c543c01)
- Data as of 2026-08-30T08:39:29.467469+00:00.