# zju3dv/PGSR

[TVCG2024] PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction

Repository: https://github.com/zju3dv/PGSR
Canonical: https://ross.abutalabs.com/products/pgsr
Homepage: https://zju3dv.github.io/pgsr/
Language: Python
License: NOASSERTION
License Family: other
Topics: 3dgs, surface-reconstruction
Last push: 2024-12-25T07:54:39+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 58
- inputs: {"age_days": 814, "days_push": 616, "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 1092, forks 95 (observed 2026-08-28T04:03:33.211936+00:00)

## What it is
PGSR is a Python implementation of a planar-based Gaussian Splatting method for efficient, high-fidelity 3D surface reconstruction from multi-view RGB images, published at TVCG 2024. It renders unbiased depth and normal maps from Gaussian primitives with geometric regularization to produce accurate meshes without requiring depth or normal priors.

## Use cases
- reconstruct 3D meshes from multi-view RGB photos
- surface reconstruction from DTU or Tanks and Temples datasets
- get high-fidelity geometry from Gaussian splatting scenes
- compare against NeRF and 3DGS-based reconstruction baselines
- reconstruct indoor and outdoor scenes with illumination variation
- fast mesh extraction from trained 3D Gaussian scenes

## When to choose
- you need accurate meshes from multi-view images without depth or normal priors
- you want faster training than NeRF-based surface reconstruction methods
- you need both high-quality rendering and geometric accuracy from one representation
- you are benchmarking surface reconstruction on DTU, Tanks and Temples, or Mip-NeRF 360

## When to avoid
- you only need novel view synthesis without mesh geometry
- you lack a CUDA-capable GPU
- you need a permissively licensed production-ready library
- you want a turnkey application with a GUI rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: graphics, image-processing, machine-learning, simulation
- domain: computer-vision, graphics, machine-learning
- platform: python, cross-platform
- tags: 3dgs, gaussian-splatting, surface-reconstruction, mesh-reconstruction, multi-view-stereo, nerf, computer-graphics, research-code, 3d-reconstruction, research, linux, gpu

## Member repositories
- zju3dv/PGSR (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:33.211936+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 2026-08-30T06:49:04.787262+00:00, confidence not recorded.
  - readme: https://github.com/zju3dv/PGSR (fetched 2026-08-28T04:03:33.211936+00:00, sha 59a8fc5d9420)
  - homepage: https://zju3dv.github.io/pgsr/ (fetched 2026-08-29T12:51:12.686547+00:00, sha 0a562b420e5f)
- Data as of 2026-08-30T08:39:29.467469+00:00.