Parametric Surface Constrained Upsampler Network for Point Cloud

TL;DR

This paper introduces a novel point cloud upsampler that incorporates explicit surface constraints via parametric surface regularization, significantly improving the quality of dense point cloud generation.

Contribution

It proposes a surface regularizer using bicubic and rotation functions to constrain generated points on underlying surfaces, enhancing upsampling and completion tasks.

Findings

Achieves state-of-the-art results on point cloud upsampling.

Effectively reduces outlier points in generated point clouds.

Demonstrates improved surface adherence in point cloud completion.

Abstract

Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the new generated points are then constrained on the underlying surface. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers - point cloud upsampling and point cloud completion for evaluation. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method. The code is available at https://github.com/corecai163/PSCU.