# FPNN

**Repository Path**: deep_learning_workpiece/FPNN

## Basic Information

- **Project Name**: FPNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-06-22
- **Last Updated**: 2020-12-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FPNN: Field Probing Neural Networks for 3D Data

Created by <a href="http://web.stanford.edu/~yangyan/" target="_blank">Yangyan Li</a>, <a href="http://www.pirk.info/" target="_blank">Soeren Pirk</a>, <a href="http://ai.stanford.edu/~haosu/" target="_blank">Hao Su</a>, <a href="http://web.stanford.edu/~rqi/" target="_blank">Charles Ruizhongtai Qi</a>, and <a href="http://geometry.stanford.edu/member/guibas/" target="_blank">Leonidas J. Guibas</a> from Stanford University.

### Introduction
We propose a light-weight way for learning features from 3D data. See more details from our <a href="http://arxiv.org/abs/1605.06240" target="_blank">research paper on arXiv</a> (was accepted to NIPS 2016).

### Usage
Check <a href="https://github.com/yangyanli/FPNN/tree/master/training_settings" target="_blank">training settings</a> for example usage of the field probing layers, as well as logs generated during our training.

### From FPNN to PointCNN
If you are interested in FPNN, we highly recommend you take a look at [PointCNN](https://github.com/yangyanli/PointCNN), which outperforms FPNN in terms of ModelNet40 classification, together with other advantages.