# Awesome3DGS/3D-Gaussian-Splatting-Papers

3D高斯论文，持续更新，欢迎交流讨论。

Repository: https://github.com/Awesome3DGS/3D-Gaussian-Splatting-Papers
Canonical: https://ross.abutalabs.com/products/3d-gaussian-splatting-papers
Language: Python
License Family: other
Topics: 3dgs, 3d-gaussian-splatting, gaussian-splatting, novel-view-synthesis
Last push: 2026-06-12T07:01:54+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 87, release rhythm 35, longevity 70
- inputs: {"age_days": 987, "days_push": 82, "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 3132, forks 123 (observed 2026-08-28T04:07:45.529935+00:00)

## What it is
A curated awesome-list tracking 3D Gaussian Splatting research papers organized by venue and year, with archives, surveys, and links to arXiv pages and code. It is a documentation resource rather than executable software.

## Use cases
- find papers on 3d gaussian splatting
- track latest gaussian splatting research by conference
- find open-source implementations of 3dgs papers
- survey novel view synthesis literature
- find survey papers on radiance field methods

## When to choose
- you need a comprehensive, venue-organized bibliography of 3DGS research
- you want links to code alongside papers
- you need historical archives of the field's output

## When to avoid
- you need a runnable 3DGS implementation or library
- you need papers published after the list stopped accepting new entries in June 2026
- you need non-Gaussian-Splatting computer vision papers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, graphics, computer-vision
- domain: computer-vision, graphics, awesome-lists, tutorials
- platform: cross-platform
- tags: 3d-gaussian-splatting, awesome-list, paper-collection, novel-view-synthesis, research-papers

## Member repositories
- Awesome3DGS/3D-Gaussian-Splatting-Papers (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:45.529935+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 2026-08-30T07:26:07.964537+00:00, confidence not recorded.
  - readme: https://github.com/Awesome3DGS/3D-Gaussian-Splatting-Papers (fetched 2026-08-28T04:07:45.529935+00:00, sha dd1a6aed3339)
- Data as of 2026-08-30T08:39:29.467469+00:00.