Abstract

Panoramic imaging has recently become an extensively used technology for the representation and exploration of indoor environments. Panoramic cameras generate omnidirectional images that provide a comprehensive 360-degree view, making them a valuable tool for applications such as virtual tours in real estate, architecture, and cultural heritage. However, constructing truly immersive experiences from panoramic images presents challenges, particularly in generating panoramic stereo pairs that offer consistent depth cues and visual comfort across all viewing directions. Traditional stereo-imaging techniques do not directly apply to spherical panoramic images, requiring complex processing to avoid artifacts that can disrupt immersion. To address these challenges, various imaging and processing technologies have been developed, including multi-camera systems and computational methods that generate stereo images from a single panoramic input. Although effective, these solutions often involve complicated hardware and processing pipelines. Recently, deep learning approaches have emerged, enabling novel view generation from single panoramic images. While these methods show promise, they have not yet been thoroughly evaluated in practical scenarios. This paper presents a series of evaluation experiments aimed at assessing different technologies for creating static stereoscopic environments from omnidirectional imagery, with a focus on 3DOF immersive exploration. A user study was conducted using a WebXR prototype and a Meta Quest 3 headset to quantitatively and qualitatively compare traditional image composition techniques with AI-based methods. Our results indicate that while traditional methods provide a satisfactory level of immersion, AI-based generation is nearing a quality level suitable for deployment in web-based environments.