# mkazhdan/PoissonRecon

Poisson Surface Reconstruction

Repository: https://github.com/mkazhdan/PoissonRecon
Canonical: https://ross.abutalabs.com/products/poissonrecon
Language: C
License: MIT
License Family: permissive
Last push: 2026-04-29T15:37:58+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 79, release rhythm 8, longevity 100
- inputs: {"age_days": 4068, "days_push": 126, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1848, forks 471 (observed 2026-08-28T04:05:43.607109+00:00)

## What it is
An implementation of Poisson Surface Reconstruction for turning oriented point clouds into watertight 3D meshes, evolved into a general adaptive multigrid finite-element solver. It supports arbitrary dimensions, element degrees, and point-wise/integrated constraints.

## Use cases
- reconstruct a 3D mesh from a scanned point cloud
- convert LiDAR or depth-sensor points into a watertight surface
- solve adaptive finite-element systems with multigrid
- generate meshes from RGB-D scan data
- process oriented point sets into implicit surfaces

## When to choose
- you need high-quality watertight surfaces from oriented point clouds
- you want a proven, well-cited geometry reconstruction algorithm
- you need a flexible adaptive multigrid finite-element solver

## When to avoid
- you need mesh repair of existing meshes rather than reconstruction from points
- you want a GPU-accelerated real-time solution
- you need a high-level Python or GUI workflow

## Facets
- artifact type: library
- maturity: stable
- function: graphics, simulation, math
- domain: graphics, computer-vision
- platform: windows, cpp, cross-platform
- tags: poisson-surface-reconstruction, 3d-reconstruction, mesh-generation, multigrid-solver, finite-elements, point-cloud, geometry-processing, algorithms, linux, macos

## Member repositories
- mkazhdan/PoissonRecon (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:43.607109+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 2026-08-30T03:17:49.001044+00:00, confidence not recorded.
  - readme: https://github.com/mkazhdan/PoissonRecon (fetched 2026-08-28T04:05:43.607109+00:00, sha 9d8b14161eae)
- Data as of 2026-08-30T08:39:29.467469+00:00.