# QingyongHu/RandLA-Net

🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

Repository: https://github.com/QingyongHu/RandLA-Net
Canonical: https://ross.abutalabs.com/products/randla-net
Language: Python
License: NOASSERTION
License Family: other
Topics: semantic-segmentation, 3d-vision, computer-vision, semantic3d, s3dis, semantickitti
Last push: 2023-07-11T22:42:23+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": 2473, "days_push": 1149, "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 1560, forks 336 (observed 2026-08-28T04:05:03.614261+00:00)

## What it is
Official TensorFlow implementation of RandLA-Net, a neural architecture for efficient semantic segmentation of large-scale 3D point clouds, published at CVPR 2020 (Oral) and IEEE TPAMI 2021. It includes training and evaluation scripts for benchmarks like S3DIS, Semantic3D, and SemanticKITTI with pre-trained models available.

## Use cases
- segment large-scale 3d point clouds semantically
- run semantic segmentation on semantickitti dataset
- evaluate point cloud segmentation on s3dis benchmark
- reproduce cvpr 2020 randla-net results
- get pre-trained model for 3d lidar scene labeling
- benchmark efficient point cloud segmentation architectures

## When to choose
- you need efficient semantic segmentation of large-scale 3D point clouds
- you want to reproduce or build on the RandLA-Net paper results
- you work with benchmarks like S3DIS, Semantic3D, or SemanticKITTI
- you need a lightweight point cloud segmentation model without heavy sampling or preprocessing

## When to avoid
- you need PyTorch or modern TensorFlow 2.x support
- you require a permissively licensed model for commercial use (CC BY-NC-SA 4.0)
- you need actively maintained code with recent dependency updates
- your project involves 2D image segmentation rather than 3D point clouds

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision
- domain: computer-vision, machine-learning, deep-learning
- platform: python
- tags: point-clouds, semantic-segmentation, tensorflow, cvpr-2020, 3d-vision, research-code, linux, gpu

## Member repositories
- QingyongHu/RandLA-Net (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:03.614261+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 2026-08-30T04:29:46.099881+00:00, confidence not recorded.
  - readme: https://github.com/QingyongHu/RandLA-Net (fetched 2026-08-28T04:05:03.614261+00:00, sha 3d53b76340c9)
- Data as of 2026-08-30T08:39:29.467469+00:00.