ECG: Edge-aware Point Cloud Completion with Graph Convolution

Pytorch Implementation Evaluated with python 3.5 and pytorch 1.2

Introduction

This work is based on our paper "ECG: Edge-aware Point Cloud Completion with Graph Convolution"

Scanned 3D point clouds for real-world scenes often suffer from noise and incompletion. Observing that prior point cloud shape completion networks overlook local geometric features, we propose our ECG - an Edge-aware point cloud Completion network with Graph convolution, which facilitates fine-grained 3D point cloud shape generation with multi-scale edge features. Our ECG consists of two consecutive stages: 1)skeleton generation and 2) details refinement. Each stage is a generation sub-network conditioned on the input incomplete point cloud. The first stage generates coarse skeletons to facilitate capturing useful edge features against noisy measurements. Subsequently, we design a deep hierarchical encoder with graph convolution to propagate multi-scale edge features for local geometric details refinement. To preserve local geometrical details while upsampling, we propose the Edge-aware Feature Expansion (EFE) module to smoothly expand/upsample point features by emphasizing their local edges. Extensive experiments show that our ECG significantly outperforms previous state-ofthe-art (SOTA) methods for point cloud completion.

Installation

1. Install required python libs
2. Downloading corresponding dataset (e.g. ShapeNet dataset; TopNet dataset; or Cascade dataset)
3. Compile pytorch 3rd-party libs

Citation

If you find our work useful in your research, please cite:

@ARTICLE{9093117,
author={L. {Pan}},
journal={IEEE Robotics and Automation Letters}, 
title={ECG: Edge-aware Point Cloud Completion with Graph Convolution}, 
year={2020},
volume={5},
number={3},
pages={4392-4398},}

Acknowledgement

We include two CD loss functions by 1 and 2, and the EMD function & expansion_penalty in our repository.

License

Our code is released under MIT License.