GitHub repository
An efficient neural network for semantic segmentation of large-scale 3D point clouds using random sampling.
What is GitHub repository?
RandLA-Net is a neural network architecture for semantic segmentation of large-scale 3D point clouds. It efficiently processes raw point data to classify each point into semantic categories like buildings, vegetation, or vehicles. The model solves the problem of handling massive point sets by using random sampling and local feature aggregation, making it practical for real-world 3D scene analysis.
Researchers and engineers in computer vision, robotics, and autonomous systems who work with 3D point cloud data and need efficient semantic segmentation for applications like urban mapping, environmental monitoring, or autonomous driving.
Developers choose RandLA-Net for its balance of high accuracy and computational efficiency, avoiding the heavy preprocessing required by other methods. Its lightweight design and state-of-the-art performance on major benchmarks make it a go-to solution for scalable 3D point cloud segmentation.
Overview
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
Use Cases
Best For
- Semantic segmentation of urban-scale 3D point clouds from LiDAR scans
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