QingyongHu/RandLA-Net
RandLA-Net is a TensorFlow neural network for efficient semantic segmentation of large-scale 3D point clouds.
Not currently ranked — collecting fresh signals.
star history
This repository provides the official implementation of RandLA-Net, a neural architecture designed for semantic segmentation of large-scale 3D point clouds. It was published at CVPR 2020 (Oral) and IEEE TPAMI 2021. The method processes 3D point cloud data (e.g., from LiDAR scans) to assign semantic labels to each point, enabling scene understanding for applications such as autonomous driving and robotics. It is implemented in TensorFlow.
Frequently asked
- What is QingyongHu/RandLA-Net?
- RandLA-Net is a TensorFlow neural network for efficient semantic segmentation of large-scale 3D point clouds.
- Is RandLA-Net open source?
- Yes — QingyongHu/RandLA-Net is an open-source project tracked on heatdrop.
- What language is RandLA-Net written in?
- QingyongHu/RandLA-Net is primarily written in Python.
- How popular is RandLA-Net?
- QingyongHu/RandLA-Net has 1.6k stars on GitHub.
- Where can I find RandLA-Net?
- QingyongHu/RandLA-Net is on GitHub at https://github.com/QingyongHu/RandLA-Net.