Unraveling Noise in 3D Point Clouds: A Comprehensive Survey

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Abstract
Noise contamination in three-dimensional data remains a persistent challenge in reconstruction and measurement pipelines, with critical implications for dimensional inspection and industrial metrology, as well as for autonomous navigation and cultural heritage documentation. Whenever a point cloud is used to measure rather than merely to visualize, the noise it carries propagates directly into the reported dimension and into its associated uncertainty. This work presents an innovative three-axis taxonomy that unifies the classification and mitigation of noise in 3D point clouds. Our framework systematically categorizes noise sources into (1) statistical models, (2) scene interferences, and (3) reconstruction imperfections, and it is designed so that any newly encountered noise type can be assigned consistently by means of three explicit criteria (origin, modeling tractability and spatio-temporal dependency). Through theoretical analysis along these three axes, we discuss the fundamental limitations, practical challenges, and adaptive strategies of each method and summarize the resulting cross-category applicability in a comparative table. Among the unresolved challenges are real-time processing of dynamic scenes, separation of overlapping noise types, and biases in learning models toward specific sensor geometries. We further argue that, in a measurement context, a denoising operator must be assessed not only by the noise it suppresses but also by the systematic surface displacement—the bias—it introduces, which aggregate distance metrics tend to conceal. This research consolidates fragmented knowledge into actionable guidelines, equipping metrology and inspection practitioners to design robust 3D measurement systems and guiding researchers toward impactful innovations in noise-adaptive sensing.

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