# Yochengliu/awesome-point-cloud-analysis

A list of papers and datasets about point cloud analysis (processing)

Repository: https://github.com/Yochengliu/awesome-point-cloud-analysis
Canonical: https://ross.abutalabs.com/products/awesome-point-cloud-analysis
License Family: other
Topics: 3d-graphics, 3d-reconstruction, 3d-registration, 3d-representation, point-cloud-processing, point-cloud-segmentation, point-cloud-registration, point-cloud-detection, point-clouds, point-cloud-classification, point-cloud-recognition, point-set-registration, point-cloud-dataset
Last push: 2023-05-19T04:34:48+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": 2688, "days_push": 1202, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4219, forks 928 (observed 2026-08-28T04:08:39.251584+00:00)

## What it is
A curated awesome list of papers, code, and datasets for 3D point cloud analysis and processing, covering tasks like classification, segmentation, detection, registration, and reconstruction. It organizes research from 2017 onward with keyword tags and links to implementations.

## Use cases
- find papers on point cloud segmentation
- research deep learning methods for 3D point clouds
- find datasets for point cloud classification
- survey point cloud registration techniques
- find code implementations of PointNet-style models
- get started with 3D point cloud research

## When to choose
- starting research on 3D point cloud analysis
- looking for a curated bibliography of point cloud papers and datasets
- finding reference implementations of classic point cloud models

## When to avoid
- you need up-to-date listings after 2020 (the successor repo awesome-point-cloud-analysis-2020 is recommended)
- you need runnable software rather than a paper list
- you need a maintained library with a license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, computer-vision, data-science
- domain: computer-vision, machine-learning, deep-learning, graphics, autonomous-vehicles, awesome-lists
- platform: cross-platform
- tags: awesome-list, point-cloud, 3d-deep-learning, papers, datasets, research

## Member repositories
- Yochengliu/awesome-point-cloud-analysis (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:39.251584+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 2026-08-29T18:22:24.746827+00:00, confidence not recorded.
  - readme: https://github.com/Yochengliu/awesome-point-cloud-analysis (fetched 2026-08-28T04:08:39.251584+00:00, sha 53794ed99079)
- Data as of 2026-08-30T08:39:29.467469+00:00.