# city-super/Scaffold-GS

[CVPR 2024 Highlight] Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering

Repository: https://github.com/city-super/Scaffold-GS
Canonical: https://ross.abutalabs.com/products/scaffold-gs
Homepage: https://city-super.github.io/scaffold-gs
Language: C++
License: NOASSERTION
License Family: other
Topics: gaussian-splatting, reconstruction, rendering, cvpr2024, 3d
Last push: 2024-09-26T03:17:34+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 72
- inputs: {"age_days": 1011, "days_push": 706, "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 1278, forks 124 (observed 2026-08-28T04:04:13.423749+00:00)

## What it is
Scaffold-GS is a research implementation of a structured 3D Gaussian splatting method that uses anchor points on a sparse voxel grid to distribute local 3D Gaussians, predicting their attributes on-the-fly based on viewing direction and distance. It achieves faster convergence, fewer primitives, and better visual quality than vanilla 3D Gaussian splatting, especially on scenes with challenging views.

## Use cases
- reconstruct 3D scenes from photos with gaussian splatting
- render novel views of a captured scene
- improve rendering quality on reflective or transparent surfaces
- train a view-adaptive 3D gaussian model from SfM points
- compare structured gaussian splatting against vanilla 3DGS
- visualize trained scenes with the SIBR viewer

## When to choose
- you need high-quality novel view synthesis with fewer gaussians and faster convergence
- your scenes contain transparency, specularity, reflection, or fine-scale detail
- you are doing research on gaussian splatting and want an anchor-based baseline
- you have a CUDA-capable Linux GPU environment for training

## When to avoid
- you need a production-ready, well-documented rendering library rather than research code
- you cannot use CUDA or a Linux-based training setup
- you need real-time rendering on low-end hardware without a GPU
- you want a simple off-the-shelf viewer without compiling SIBR_viewers

## Facets
- artifact type: library
- maturity: active
- function: graphics, image-processing, machine-learning, deep-learning
- domain: computer-vision, graphics, machine-learning
- platform: python, cpp
- tags: gaussian-splatting, 3d-reconstruction, novel-view-synthesis, cvpr2024, neural-rendering, research-code, research, linux, gpu

## Member repositories
- city-super/Scaffold-GS (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:13.423749+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 2026-08-30T05:02:44.967891+00:00, confidence not recorded.
  - readme: https://github.com/city-super/Scaffold-GS (fetched 2026-08-28T04:04:13.423749+00:00, sha c2bbe54d17c6)
  - homepage: https://city-super.github.io/scaffold-gs (fetched 2026-08-29T12:13:24.788688+00:00, sha f162f3d75744)
- Data as of 2026-08-30T08:39:29.467469+00:00.