This repo aims to provide experiments and codes on point cloud feature learning.
- Unsupervised Point Cloud Reconstruction for Classific Feature Learning
- Point Cloud Segmentation for Classific Feature Learning
- coming soon
Can unsupervised point cloud reconstruction extract features suitable for classification?
This work aims to show whether learning a unsupervised point cloud reconstruction task, for example FoldingNet, is able to extract features performing well in classification. We do all experiments under the framework of FoldingNet.
The key contributions of this work are as follows:
- We provide a pytorch reimplementation for FoldingNet.
- We also use source points for decoder from sphere surface and gaussian distribution. Results show that source points from sphere surface can reconstruct better.
- We do experiments using DGCNN as encoder and provide the classification performance for linear SVM classifier. The transfer dataset performance is better than the state-of-the-art unsupervised methods. We also train our best unsupervised model supervisedly, our unsupervised results still win out.
- We illustrate that better reconstruction results do not correspond with better feature for classfication.
See this for details.
This work aims to show whether learning a point cloud segmentation task is able to extract features performing well in classification. We do all experiments under the framework of DGCNN.
We also do experiments to see whether learning segmentation on meaningful point clouds assembled by some base point clouds can help to learning better features for base point clouds.
The key contributions of this work are as follows:
- Since the network provided by DGCNN for segmentation is supervised, we provide an revised DGCNN segmentation network with no category label.
- When segmentation is trained and tested on intact point clouds, the trained model can help to extract better features.
- When segmentation is trained on intact point clouds and tested on base point clouds, the trained model also can help to extract better features.
See this for details.