# Totoro97/NeuS

Code release for NeuS

Repository: https://github.com/Totoro97/NeuS
Canonical: https://ross.abutalabs.com/products/neus
Language: Python
License: MIT
License Family: permissive
Last push: 2024-02-28T08:17:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1837, "days_push": 917, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1777, forks 222 (observed 2026-08-28T04:05:34.767669+00:00)

## What it is
Official PyTorch implementation of NeuS, a neural implicit surface reconstruction method that learns SDF-based surfaces via volume rendering from multi-view 2D images. It supports training with or without masks and exports reconstructed meshes as PLY files.

## Use cases
- reconstruct 3d surfaces from multi-view images
- neural implicit surface reconstruction from photos
- extract meshes from 2d image sets
- 3d reconstruction without depth sensors
- view interpolation from multi-view captures
- reproduce NeuS paper results

## When to choose
- you need high-fidelity object or scene reconstruction from calibrated multi-view images
- you want a research baseline for neural implicit surface methods
- you have masks or can run mask-free training on your captures

## When to avoid
- you need real-time reconstruction
- you lack GPU resources or calibrated camera parameters
- you want a production-ready photogrammetry pipeline rather than research code

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, computer-vision, image-processing, graphics
- domain: computer-vision, deep-learning, graphics, machine-learning
- platform: python
- tags: neural-implicit-surfaces, neural-rendering, 3d-reconstruction, volume-rendering, sdf, multi-view-stereo, research-code, linux, gpu

## Member repositories
- Totoro97/NeuS (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.767669+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 2026-08-30T03:25:12.611917+00:00, confidence not recorded.
  - readme: https://github.com/Totoro97/NeuS (fetched 2026-08-28T04:05:34.767669+00:00, sha 808d3505ad6e)
- Data as of 2026-08-30T08:39:29.467469+00:00.