# zju3dv/street_gaussians

[ECCV 2024] Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting

Repository: https://github.com/zju3dv/street_gaussians
Canonical: https://ross.abutalabs.com/products/street_gaussians
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-07-04T09:26:23+00:00

## Health v2 (maintenance only)
Score: 40/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 30, release rhythm 35, longevity 70
- inputs: {"age_days": 980, "days_push": 425, "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 1388, forks 108 (observed 2026-08-28T04:04:35.223034+00:00)

## What it is
Street Gaussians is a research implementation of the ECCV 2024 paper 'Modeling Dynamic Urban Scenes with Gaussian Splatting', which reconstructs dynamic street scenes from autonomous driving data using 3D Gaussian splatting. It processes Waymo Open Dataset sequences to model static backgrounds and dynamic objects as separate Gaussian representations for novel view synthesis.

## Use cases
- reconstruct dynamic urban driving scenes in 3D
- render novel views of street scenes from Waymo data
- model moving vehicles and static backgrounds separately with Gaussian splatting
- research on dynamic scene reconstruction for autonomous driving
- compare against NeRF-based methods like EmerNeRF on driving benchmarks
- visualize 4D street scene reconstructions

## When to choose
- you need state-of-the-art dynamic urban scene reconstruction from driving datasets
- you work with the Waymo Open Dataset and want Gaussian splatting baselines
- you are researching 3D/4D scene reconstruction or autonomous driving simulation
- you want editable, decomposed representations of dynamic objects in street scenes

## When to avoid
- you need a production-ready or well-supported end-user application
- you lack a CUDA-capable GPU or cannot handle large driving datasets
- you need real-time reconstruction rather than offline training
- you want a general-purpose 3D reconstruction tool for arbitrary scenes

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, graphics, simulation, image-processing
- domain: computer-vision, autonomous-vehicles, deep-learning, graphics
- platform: python
- tags: gaussian-splatting, 3d-reconstruction, neural-rendering, waymo-open-dataset, urban-scenes, eccv-2024, research-code, linux, gpu

## Member repositories
- zju3dv/street_gaussians (main) score 40

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:35.223034+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 2026-08-30T04:39:48.126622+00:00, confidence not recorded.
  - readme: https://github.com/zju3dv/street_gaussians (fetched 2026-08-28T04:04:35.223034+00:00, sha 322ee67b1b04)
- Data as of 2026-08-30T08:39:29.467469+00:00.