Deep Learning For Point Cloud Denoising: A Survey

TL;DR

This survey comprehensively reviews deep learning methods for point cloud denoising, highlighting key challenges, existing approaches, and future research directions to improve noise removal in 3D data.

Contribution

It systematically summarizes recent DL-based PCD methods, proposes a taxonomy, and discusses challenges and future directions in the field.

Findings

Deep learning models outperform traditional denoising methods.

A taxonomy for DL-based PCD methods is proposed.

Identifies key challenges and future research directions.

Abstract

Real-world environment-derived point clouds invariably exhibit noise across varying modalities and intensities. Hence, point cloud denoising (PCD) is essential as a preprocessing step to improve downstream task performance. Deep learning (DL)-based PCD models, known for their strong representation capabilities and flexible architectures, have surpassed traditional methods in denoising performance. To our best knowledge, despite recent advances in performance, no comprehensive survey systematically summarizes the developments of DL-based PCD. To fill the gap, this paper seeks to identify key challenges in DL-based PCD, summarizes the main contributions of existing methods, and proposes a taxonomy tailored to denoising tasks. To achieve this goal, we formulate PCD as a two-step process: outlier removal and surface noise restoration, encompassing most scenarios and requirements of PCD. Additionally, we compare methods in terms of similarities, differences, and respective advantages. Finally, we discuss research limitations and future directions, offering insights for further advancements in PCD.