Created by Yangyan Li, Soeren Pirk, Hao Su, Charles Ruizhongtai Qi, and Leonidas J. Guibas from Stanford University.

We propose a light-weight way for learning features from 3D data. See more details from our research paper on arXiv (was accepted to NIPS 2016).

Check training settings for example usage of the field probing layers, as well as logs generated during our training.

If you are interested in FPNN, we highly recommend you take a look at PointCNN, which outperforms FPNN in terms of ModelNet40 classification, together with other advantages.