Awesome
MaST-Pre
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos (ICCV 2023)
Visualizations of Reconstruction Results. For each action sample, the ground truth is on the left, and the reconstruction result at 75% masking ratio is on the right. <br/> <img src="https://github.com/JohnsonSign/MaST-Pre/blob/main/images/1.gif" width="300"> <img src="https://github.com/JohnsonSign/MaST-Pre/blob/main/images/2.gif" width="300"><br/> <img src="https://github.com/JohnsonSign/MaST-Pre/blob/main/images/3.gif" width="300"> <img src="https://github.com/JohnsonSign/MaST-Pre/blob/main/images/4.gif" width="300">
Installation
The code is tested with Python 3.7.12, PyTorch 1.7.1, GCC 9.4.0, and CUDA 10.2.
Compile the CUDA layers for PointNet++ and Chamfer_Distance_Loss:
cd modules
python setup.py install
cd ./extensions/chamfer_dist
python setup.py install
Related Repositories
We thank the authors of related repositories: