Designing and Evaluating a Mesh Simplification Algorithm for Virtual Reality

Abstract

With the increasing accessibility of the mobile head-mounted displays (HMDs), mobile virtual reality (VR) systems are finding applications in various areas. However, mobile HMDs are highly constrained with limited graphics processing units (GPUs) and low processing power and onboard memory. Hence, VR developers must be cognizant of the number of polygons contained within their virtual environments to avoid rendering at low frame rates and inducing simulator sickness. The most robust and rapid approach to keeping the overall number of polygons low is to use mesh simplification algorithms to create low-poly versions of preexisting, high-poly models. Unfortunately, most existing mesh simplification algorithms cannot adequately handle meshes with lots of boundaries or nonmanifold meshes, which are common attributes of many 3D models. In this article, we present QEM 4VR , a high-fidelity mesh simplification algorithm specifically designed for VR. This algorithm addresses the deficiencies of prior quadric error metric (QEM) approaches by leveraging the insight that the most relevant boundary edges lie along curvatures while linear boundary edges can be collapsed. Additionally, our algorithm preserves key surface properties, such as normals, texture coordinates, colors, and materials, as it preprocesses 3D models and generates their low-poly approximations offline. We evaluated the effectiveness of our QEM 4VR algorithm by comparing its simplified-mesh results to those of prior QEM variations in terms of geometric approximation error, texture error, progressive approximation errors, frame rate impact, and perceptual quality measures. We found that QEM 4VR consistently yielded simplified meshes with less geometric approximation error and texture error than the prior QEM variations. It afforded better frame rates than QEM variations with boundary preservation constraints that create unnecessary lower bounds on overall polygon count reduction. Our evaluation revealed that QEM 4VR did not fair well in terms of existing perceptual distance measurements, but human-based inspections demonstrate that these algorithmic measurements are not suitable substitutes for actual human perception. In turn, we present a user-based methodology for evaluating the perceptual qualities of mesh simplification algorithms. CCS Concepts: • Computing methodologies → Mesh geometry models;

Digital body preservation: Technique and applications

“…More specifically in this article, the quadric error metric simplification technique with preservation of texture was able to tackle the data size issues. Based on a fidelity assessment by eight anatomy experts, high-fidelity models in this article could be reduced up to 10% of their size and beyond, reverberating the findings of Bahirat and colleagues who found that meshes could be simplified to 10% of their original polygon totals and beyond (Bahirat et al, 2018).…”

“…According to Bahirat and colleagues, meshes can be easily simplified to 10% of their original polygon totals and beyond, preserving high‐fidelity appearances compared to the original high polygon model (Bahirat et al, 2018). In this report, a survey regarding the physical fidelity of the original and simplified surface scanned models was answered by independent anatomists to evaluate the impact of mesh simplification on the models.…”

Mesh Simplification Algorithm Based on the Quadratic Error Metric and Triangle Collapse

“…Jong et al combined vertex torsion detection with the quadratic error generated by vertex contraction to simplify the mesh [34]. Bahirat et al made up for the defects of the quadratic error method by introducing curvature, normal lines and texture features [35]. Based on discrete differential geometry, Wang et al introduced the concept of adaptive weight into the quadratic error measure and realized half-fold simplification under the greedy framework [36].…”

OpenUVR: an Open-Source System Framework for Untethered Virtual Reality Applications

“…This paper did not explore optimizations for VR-content generation because other researchers have already succeeded in this area; however, the parallelization strategies of VR graphics engines do offer opportunities for improvement, notably with respect to picture-frame tiling. The graphics pipeline can be optimized by detecting redundant, identical tiles [41], [42], [43], by separating stable objects from frequently updated objects [44], [45], or by prioritizing image areas that attract user attention [46], [47]. Rollback-free value prediction [48], Potluck [49], AxGames [50], and PATU [51] apply approximate computing techniques to image elements that do not significantly affect UX.…”