RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

This repository contains a PyTorch-lightning implementation of RandLA-Net on S3DIS and Semantickitti.

This repository is mainly based on the repository

Preparation(S3DIS for example)

1. Clone this repository
2. Install some Python dependencies, such as scikit-learn. All packages can be installed with pip.
3. env : ubuntu 18.04, python 3.7.16, torch 1.12.1, numpy 1.21.5, torchvision 0.13.1, scikit-learn 0.22.2, pandas 1.3.5, tqdm 4.64.1
4. Install python functions. the functions and the codes are copied from the official implementation with Tensorflow.

sh compile_op.sh

5. Attention: please check out ./utils/nearest_neighbors/lib/python/KNN_NanoFLANN-0.0.0-py3.7-linux-x86_64.egg/ and copy the .so file to the parent folder (update in 2023.2.23: We provide a .so file for python3.7, and you don't need to copy(even compile the cpp code) if you are using python3.7)
6. Download the Stanford3dDataset_v1.2_Aligned_Version dataset, and preprocess the data:

  python utils/data_prepare_s3dis.py

Note: Please change the dataset path in the 'data_prepare_s3dis.py' with your own path.

Train a model(S3DIS for example)

cd scripts/
sh train_S3DIS.sh

Test a model(S3DIS for example)

Results

S3DIS

SemanticKITTI