# jlblancoc/nanoflann

nanoflann: a C++11 header-only library for Nearest Neighbor (NN) search with KD-trees

Repository: https://github.com/jlblancoc/nanoflann
Canonical: https://ross.abutalabs.com/products/nanoflann
Language: C++
License: NOASSERTION
License Family: other
Topics: c-plus-plus, kd-trees, point-clouds, cpp, nanoflann
Last push: 2026-08-25T20:16:26+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 4507, "days_push": 8, "days_rel": 25, "gap_med": 40, "n_releases_24m": 10}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2670, forks 525 (observed 2026-08-28T04:07:09.414952+00:00)

## What it is
nanoflann is a C++11 header-only library for fast nearest neighbor search using KD-trees. It is designed for efficient queries over point clouds and high-dimensional datasets without external dependencies.

## Use cases
- find nearest neighbors in a point cloud
- kdtree search in C++ without dependencies
- fast kNN queries for 3D point data
- nearest neighbor search for robotics SLAM
- header-only spatial indexing library
- approximate and exact k-nearest-neighbor lookup

## When to choose
- you need a lightweight, header-only C++ nearest neighbor library
- you work with point clouds or high-dimensional numeric data
- you want zero external dependencies and easy integration

## When to avoid
- you need GPU-accelerated or distributed nearest neighbor search
- you need a full linear algebra or geometry processing library
- your data is non-numeric or requires custom distance metrics beyond supported ones

## Facets
- artifact type: library
- maturity: stable
- function: search-engine, math, developer-tools
- domain: computer-vision, robotics, performance
- platform: cpp, cross-platform, windows
- tags: header-only, kd-trees, nearest-neighbor, point-clouds, c-plus-plus, algorithms, linux, macos

## Member repositories
- jlblancoc/nanoflann (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:09.414952+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 2026-08-30T02:18:10.596979+00:00, confidence not recorded.
  - readme: https://github.com/jlblancoc/nanoflann (fetched 2026-08-28T04:07:09.414952+00:00, sha c92cbd2d26c2)
- Data as of 2026-08-30T08:39:29.467469+00:00.