Subjective quality assessment of textured human full-body 3D-reconstructions

Abstract

Abstract-Geometry and texture resolution are two common system parameters of any modern volumetric 3D reconstruction pipeline. In tele-immersive applications, besides their apparent impact on the visual quality of the output 3D mesh, their absolute values implicitly influence the computational load of the whole tele-immersion pipeline from acquisition to 3D reconstruction, compression and transmission. Thus, tuning those parameters to an optimal combination has evident benefits. In this paper, we conduct a subjective experiment to assess the visual quality of textured human 3D-reconstructed meshes that are produced by a volumetric 3D reconstruction algorithm as a joint function of the geometry and texture resolution production parameters. The experiment is based on the forced choice pairwise comparison methodology on prerendered views of the real-time reconstructed meshes within the context of human performance capture. We analyze the pairwise comparison data and establish a ranking of the parameter space and, thus also, a mapping from the parameters to the subjective visual quality. The results of this study may be utilized to tune the parameters of the real-time 3D reconstruction pipeline, optimizing for the best balance between visual quality, bandwidth and overall performance.

Subjective and objective quality assessment for volumetric video

“…Pioneering subjective quality tests involving meshes were conducted on still rendered images [33,34]; those two early studies both assessed the visual impact of simplification artifacts and concerned static and geometry-only meshes; the same applies in [35], which evaluated the impact of geometry compression. Subsequent passive interaction experiments considered meshes with color/texture attributes [36][37][38][39][40][41] or dynamic meshes [8,42,43]. These studies are detailed below.…”

“…Watson et al [33] used still rendered images to evaluate the visual impact of mesh simplification using the DSIS methodology. Váša and Rus [35] and Doumanoglou et al [38] also used still rendered images in their studies to evaluate the impairment of geometry compression and the visual impact of geometry and texture resolution on the quality of textured human body reconstructed meshes, respectively. Both of these studies considered a pairwise comparison methodology.…”

Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric

“…Subjective quality tests involving 3D models were initially introduced on meshes, more precisely on geometry-only models, to assess the artifacts induced by the simplification, smoothing, watermarking and compression [Christaki et al 2018;Corsini et al 2007;Lavoué 2009;Lavoué et al 2006;Rogowitz and Rushmeier 2001;Silva et al 2009;Torkhani et al 2015;Vanhoey et al 2017;Váša and Rus 2012;Váša and Skala 2011;Watson 2001]. Little work considered meshes with color attributes (vertex color or texture) [Doumanoglou et al 2018;Guo et al 2016;Gutiérrez et al 2020;Nehmé et al 2020;Nehmé et al 2021b;Pan et al 2005;Zerman et al 2020].…”

Impact of Self-View Latency on Quality of Experience: Analysis of Natural Interaction in XR Environments

“…In addition, delay can harm both quality of experience and user performance. Specifically, the delay related to the self representation in XR is specially interesting as seeing yourself in the virtual reality is important to preserve the user's immersion [15,16]. Delay in immersive environments has been addressed by other studies.…”