3D Indoor Scene Geometry Estimation from a Single Omnidirectional Image: A Comprehensive Survey

Abstract

This paper surveys the technology used in three-dimensional indoor scene geometry estimation from a single 360° omnidirectional image, which is pivotal in extracting 3D structural information from indoor environments. The technology transforms omnidirectional data into a 3D model, depicting spatial structure, object positions, and scene layout. Its significance spans various domains, including virtual reality (VR), augmented reality (AR), mixed reality (MR), game development, urban planning, and robot navigation. We begin by revisiting foundational concepts of omnidirectional imaging and detailing the problems, applications, and challenges in this field. Our review categorizes the fundamental tasks of structure recovery, depth estimation, and layout recovery. We also review pertinent datasets and evaluation metrics, providing the latest research as a reference. Finally, we summarize the field and discuss potential future trends to inform and guide further research.