Performance Evaluation and Optimization of 3D Models from Low-Cost 3D Scanning Technologies for Virtual Reality and Metaverse E-Commerce

Abstract

Virtual Reality (VR) is and will be a key driver in the evolution of e-commerce, providing an immersive and gamified shopping experience. However, for VR shopping spaces to become a reality, retailers’ product catalogues must first be digitised into 3D models. While this may be a simple task for retail giants, it can be a major obstacle for small retailers, whose human and financial resources are often more limited, making them less competitive. Therefore, this paper presents an analysis of low-cost scanning technologies for small business owners to digitise their products and make them available on VR shopping platforms, with the aim of helping improve the competitiveness of small businesses through VR and Artificial Intelligence (AI). The technologies to be considered are photogrammetry, LiDAR sensors and NeRF.In addition to investigating which technology provides the best visual quality of 3D models based on metrics and quantitative results, these models must also offer good performance in commercial VR headsets. In this way, we also analyse the performance of such models when running on Meta Quest 2, Quest Pro and Quest 3 headsets (Reality Labs, Reality Labs, CA, USA) to determine their feasibility and provide use cases for each type of model from a scalability point of view. Finally, our work describes a model optimisation process that reduce the polygon count and texture size of high-poly models, converting them into more performance-friendly versions without significantly compromising visual quality.