DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis

Here is the PyTorch implementation of the paper Very Deep Local Aggregation Networks for Point Cloud Analysis.

Demo

Highlights

• Jan, 2024: We released the model code of DeepLA-Net. And the next step will be released after receiving clear review comments.
• Dec, 2023: We released our project repo for DeepLA-Net, if you have any questions related to our work, please feel free to open an issue.

Schedule

To make our polished code and reproduced experiments available as soon as possible, this time we will release what we already finished immediately after a validation instead of releasing them together after all work is done. We list a task list as follows:

• Release model code of DeepLA-Net;
• Release scratched config of semantic segmentation;
  • ScanNet
  • S3DIS
• Release scratched config of object classification;
• Release scratched config of part segmentation;

Dependencies

• Ubuntu: 18.04 or higher
• PyTorch: 1.11.0
• CUDA: 11.3
• To create conda environment, command as follows:

  conda create -n deeplanet python=3.8
  conda activate deeplanet
  conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
  conda install llvm-openmp
  conda install plyfile=0.8.1 scipy=1.10.1 h5py=3.8.0 ninja
  

• Install pointnet2_ops:

  cd utils/pointnet2_ops_lib/
  pip install .