marian42/mesh_to_sdf

Health v2 · maintenance only

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Adoption not part of the score

1240 stars · 124 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity stable

graphics simulation machine-learning image-processing computer-vision graphics machine-learning deep-learning python cross-platform signed-distance-field mesh-processing voxelization 3d geometry sdf trimesh

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