FuNet: Multi-Feature Fusion for Point Cloud Completion Network

Abstract

The densification of a point cloud is a crucial challenge in visual applications, particularly when estimating a complete and dense point cloud from a local and incomplete one. This paper introduces a point cloud completion network named FuNet to address this issue. Current point cloud completion networks adopt various methodologies, including point-based processing and convolution-based processing. Unlike traditional shape completion approaches, FuNet combines point-based processing and convolution-based processing to extract their features, and fuses them through an attention module to generate a complete point cloud from 1024 points to 16,384 points. The experimental results show that when comparing the optimal completion networks, FuNet decreases the CD by 5.17% and increases the F-score by 4.75% on the ShapeNet dataset. In addition, FuNet achieves better results in most categories on a small sample dataset.