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

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.