Point cloud denoising review: from classical to deep learning-based approaches

Abstract

Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of point cloud denoising techniques. In this article, we first provide a comprehensive survey on state-of-the-art denoising solutions, which are mainly categorized into three classes: filter-based, optimization-based, and deep learning-based techniques. Methods of each class are analyzed and discussed in detail. This is done using a benchmark on different denoising models, taking into account different aspects of denoising challenges. We also review two kinds of quality assessment methods designed for evaluating denoising quality. A comprehensive comparison is performed to cover several popular or state-of-the-art methods, together with insightful observations. Finally, we discuss open challenges and future research directions in identifying new point cloud denoising strategies.