Abstract
A point cloud is an effective 3D geometrical presentation of data paired with different attributes such as transparency, normal and color of each point. The imperfect acquisition process of a 3D point cloud usually generates a significant amount of noise. Hence, point cloud denoising has received a lot of attention. Most of the existing techniques perform point cloud denoising based only on the geometry information of the neighbouring points; there are very few works considering the problem of denoising of color attributes of a point cloud, and taking advantage of the correlation between geometry and color. In this article, we introduce a novel non-iterative set-up for the denoising of point cloud based on spectral graph wavelet transform (SGW) that jointly exploits geometry and color to perform denoising of geometry and color attributes in graph spectral domain. The designed framework is based on the construction of joint geometry and color graph that compacts the energy of smooth graph signals in the low-frequency bands. The noise is then removed from the spectral graph wavelet coefficients by applying data-driven adaptive soft-thresholding. Extensive simulation results show that the proposed denoising technique significantly outperforms state-of-the-art methods using both subjective and objective quality metrics.
Highlights
Point clouds are considered as an efficient and useful technique to render volumetric data in 3D space
Only one work is available for the color denoising of a point cloud using Graph Laplacian regularizer (GLR) coupled with alternating direction method of multipliers [19]; still, there are various applications employing both the geometry and color attribute of a point cloud
2) We propose a 3D point cloud geometry denoising problem using spectral graph wavelet transform (SGW), for which we have constructed a joint geometry/color graph
Summary
Point clouds are considered as an efficient and useful technique to render volumetric data in 3D space. We present a novel non-iterative algorithm for the point cloud geometry and color denoising problem, which jointly uses geometry and color attributes of points. Extensive simulation results on synthetic and real-world point clouds show that our proposed algorithm for color-only and geometry-only denoising outperforms state-of-the-art techniques using both subjective and objective quality metrics. 2) We propose a 3D point cloud geometry denoising problem using SGW, for which we have constructed a joint geometry/color graph.
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