Noisemaker 3D: Comprehensive Framework for Mesh Noise Generation

Abstract

In this article, we present a comprehensive library for generating node and topological noise in meshes. The library provides a versatile tool for creating corrupted mesh datasets, which are essential for learning-based denoising algorithms. Our main contributions include cluster and patch noise generation techniques for mesh topology corruption. Cluster generation supports two modes: separated and merged clusters. We also compare the node noise generated by the library to real noise from a scanned object dataset. Finally, we create a noisy object dataset using the library and test it with filter-based and machine learning-based denoising methods.