CMD-Net: Self-Supervised Category-Level 3D Shape Denoising through Canonicalization

Abstract

Point clouds provide a compact representation of 3D shapes however, the imperfections in acquisition processes corrupt point clouds by noise and give rise to a decrease in their power for representing 3D shapes. Learning-based denoising methods operate displacement prediction and suffer from shrinkage and outliers. In addition, they require pre-aligned datasets. In this paper, we present a self-supervised learning-based method, Canonical Mapping and Denoising Network (CMD-Net), and address category-level 3D shape denoising through canonicalization. We formulate denoising as a 3D semantic shape correspondence estimation task where we explore ordered 3D intrinsic structure points. Utilizing the convex hull of the explored structure points, the corruption on objects’ surfaces is eliminated. Our method is capable of canonicalizing noise-corrupted clouds under arbitrary rotations, therefore circumventing the requirement on pre-aligned data. The complete model learns to canonicalize the input through a novel transformer that serves as a proxy in the downstream denoising task. The analyses on the experiments validate the promising performance of the presented method on both synthetic and real data. We show that our method can not only eliminate corruption, but also remove clutter from the test data. We additionally create a novel dataset for the problem in hand and will make it publicly available in our project web-page.

Keywords

• point cloud denoising
• canonical mapping
• point cloud structure learning