# marian42/mesh_to_sdf

Calculate signed distance fields for arbitrary meshes

Repository: https://github.com/marian42/mesh_to_sdf
Canonical: https://ross.abutalabs.com/products/mesh_to_sdf
Homepage: https://pypi.org/project/mesh-to-sdf/
Language: Python
License: MIT
License Family: permissive
Last push: 2024-03-10T20:56:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2421, "days_push": 906, "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 1240, forks 124 (observed 2026-08-28T04:04:06.169061+00:00)

## What it is
A Python library that computes approximate signed distance fields (SDFs) for arbitrary triangle meshes, including non-watertight, self-intersecting, and non-manifold geometry. It supports voxelizing meshes into SDF grids and sampling SDF points near surfaces, as used in the DeepSDF paper.

## Use cases
- calculate signed distance fields for meshes with holes
- voxelize a 3D mesh into an SDF grid
- sample SDF points near a mesh surface for DeepSDF training
- process non-watertight or self-intersecting meshes
- reconstruct meshes from SDF volumes with marching cubes
- prepare 3D shape data for neural implicit surface learning

## When to choose
- your meshes are not watertight or have inconsistent face orientation
- you need SDF data for deep learning on 3D shapes
- you want a simple pip-installable Python solution for mesh-to-SDF conversion

## When to avoid
- you need exact, high-precision SDFs rather than approximations
- you work with watertight meshes and need maximum accuracy
- you need GPU-accelerated or real-time SDF computation

## Facets
- artifact type: library
- maturity: stable
- function: graphics, simulation, machine-learning, image-processing
- domain: computer-vision, graphics, machine-learning, deep-learning
- platform: python, cross-platform
- tags: signed-distance-field, mesh-processing, voxelization, 3d, geometry, sdf, trimesh

## Member repositories
- marian42/mesh_to_sdf (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.169061+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 2026-08-30T05:09:53.511475+00:00, confidence not recorded.
  - readme: https://github.com/marian42/mesh_to_sdf (fetched 2026-08-28T04:04:06.169061+00:00, sha 40208866b47d)
  - homepage: https://pypi.org/project/mesh-to-sdf/ (fetched 2026-08-29T12:21:03.949483+00:00, sha 4b4e8fead74a)
  - registry_pypi: https://pypi.org/pypi/mesh_to_sdf/json (fetched 2026-08-29T12:21:03.951919+00:00, sha ff89f6a86143)
- Data as of 2026-08-30T08:39:29.467469+00:00.