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VR Panorama Inpainting: A Feasibility Study of High-Resolution View Synthesis for Virtual Reality
Lillian Maria Eagan (University of Otago), Nam Nguyen (California Polytechnic State University), Angela Victoria Chen (California Polytechnic State University), Theresa Zhu (California Polytechnic State University), Seth Maxwell Johnson (California Polytechnic State University), Pranav Reddy Dumpa (California Polytechnic State University), Benjamin Jonson Geil (California Polytechnic State University), Jonathan Ventura (California Polytechnic State University), Stefanie Zollmann (University of Otago)
Abstract: In this work, we propose a machine learning-based pipeline for generating a 3D-colored mesh representation of a scene from a single-input panorama. Our approach allows us to create a fully immersive Virtual Reality (VR) experiences. Unlike neural rendering approaches that are constrained by lowresolution inputs and offline processing, we focus on high-quality resolution inputs (4 K) and real-time rendering on VR headsets by estimating a depth map and creating a layered depth image (LDI) representation. We iteratively fill unseen regions behind objects through a cutting and inpainting process, after which the LDI is converted into an optimized, texture-mapped 3D mesh, resulting in a compact and efficient representation. We assess the feasibility of this approach through a quantitative evaluation with a user study in VR, focusing on the practicality, visual quality, and perceived presence of the generated scenes.