VIPDA: A Visually Driven Point Cloud Denoising Algorithm Based on Anisotropic Point Cloud Filtering - Cattai, Tiziana; Delfino, Alessandro; Scarano, Gaetano; Colonnese, Stefania (Frontiers in Signal Processing, 2022)
Point Clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which has a growing interest in recent applications, such as e-health or autonomous means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) are affected by noise. The presence of perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel VIsually driven Point cloud Denoising Algorithm (VIPDA) inspired to visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies an harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suitable a set of neighbours and to drive the denoising in accordance to the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-the-art methods, and we verify that VIPDA outperforms the others in terms of signal-to-noise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces.