3D Visual Content Datasets

Fliegel, Karel; Battisti, Federica; Carli, Marco; Gelautz, Margrit; Krasula, Lukáš; Callet, Patrick Le; Zlokolica, Vladimir

doi:10.1007/978-3-319-77842-6_11

Cited by 3 publications

(1 citation statement)

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“…This section focuses on the analysis of the experimental results, both visual and objective, on static real-world point clouds available in the JPEG PLENO (GTI-UTM) database [58] and Greyc dataset of synthetic point clouds [59]. Each point cloud in real-world and synthetic dataset consisting of geometry and color attributes.…”

Section: Resultsmentioning

confidence: 99%

Joint Geometry and Color Point Cloud Denoising Based on Graph Wavelets

Irfan

Magli

2021

IEEE Access

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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 paper, 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.

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Section: Resultsmentioning

confidence: 99%