RandLA-Net (efficient point cloud segmentation)
RandLA-Net is a deep learning architecture designed for efficient and scalable semantic segmentation of large-scale 3D point clouds. It employs random sampling and local feature aggregation to achieve high accuracy with reduced computational cost.