Joint geometry and color point cloud denoising based on graph wavelets

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

No-reference Point Cloud Geometry Quality Assessment Based on Pairwise Rank Learning

Objective geometry quality assessment of point clouds is essential to evaluate the performance of a wide range of point cloud-based solutions, such as denoising, simplification, reconstruction, and watermarking. Existing point cloud quality assessment (PCQA) methods dedicate to assigning absolute quality scores to distorted point clouds. Their performance is strongly reliant on the quality and quantity of subjective ground-truth scores for training, which are challenging to gather and have been shown to be imprecise, biased, and inconsistent. Furthermore, the majority of existing objective geometry quality assessment approaches are carried out by full-reference traditional metrics. So far, point-based no-reference geometry-only quality assessment techniques have not yet been investigated. This paper presents PRL-GQA, the first pairwise learning framework for no-reference geometry-only quality assessment of point clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a siamese deep architecture, which takes as input a pair of point clouds and outputs their rank order. Each siamese architecture branch is a geometry quality assessment network (GQANet), which is designed to extract multi-scale quality-aware geometric features and output a quality index for the input point cloud. Then, based on the predicted quality indexes, a pairwise rank learning module is introduced to rank the relative quality of a pair of degraded point clouds.Extensive experiments demonstrate the effectiveness of the proposed PRL-GQA framework. Furthermore, the results also show that the fine-tuned no-reference GQANet performs competitively when compared to existing full-reference geometry quality assessment metrics

Point Cloud Denoising using Joint Geometry/Color Graph Wavelets

A point cloud is a 3D geometric signal representation associated with other attributes such as color, normal, trans parency. Point clouds usually suffer from noise due to imperfect acquisition systems. Based on the notion that geometry and color are correlated, we present a novel non-iterative framework for point cloud denoising using Spectral Graph Wavelet transform (SGW) that takes advantage of this correlation and performs denoising in the graph frequency domain. The proposed approach is based on the design of a joint geometry and color graph that compacts the energy of smooth graph signals in low-frequency bands. We then apply soft-thresholding to remove the noise from the spectral graph wavelet coefficients. Experimental results show that the proposed technique significantly outperforms state-of-the-art methods

Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions

In this paper, a quality evaluation of three point cloud coding solutions based on machine learning technology is presented, notably, ADLPCC, PCC_GEO_CNN, and PCGC, as well as LUT_SR, which uses multi-resolution Look-Up Tables. Moreover, the MPEG G-PCC was used as an anchor. A set of six point clouds, representing both landscapes and objects were coded using the five encoders at different bit rates, and a subjective test, where the distorted and reference point clouds were rotated in a video sequence side by side, is carried out to assess their performance. Furthermore, the performance of point cloud objective quality metrics that usually provide a good representation of the coded content is analyzed against the subjective evaluation results. The obtained results suggest that some of these metrics fail to provide a good representation of the perceived quality, and thus are not suitable to evaluate some distortions created by machine learning-based solutions. A comparison between the analyzed metrics and the type of represented scene or codec is also presented.This research was funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio

A review of existing evaluation methods for point clouds quality

This paper analyzes the existing evaluation methods for the point cloud quality and a new discussion regarding their applicability to aerial photographs is opened. Some of these methods are chosen based on practical issues and applied to a pair of reconstructions. The principal conclusion is that objective methods are the most interesting in photogrammetry applications, particularly the comparison between two point clouds.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI