Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance perception and immersion. However, the influence of eye height in pre-captured real environments has received less attention due to the difficulty of altering the perspective after finishing the capture process. To explore this influence, we first propose a pilot study that captures real environments with multiple eye heights and asks participants to judge the egocentric distances and immersion. If a significant influence is confirmed, an effective image-based approach to adapt pre-captured real-world environments to the user's eye height would be desirable. Motivated by the study, we propose a learning-based approach for synthesizing novel views for omnidirectional images with altered eye heights. This approach employs a multitask architecture that learns depth and semantic segmentation in two formats, and generates high-quality depth and semantic segmentation to facilitate the inpainting stage. With the improved omnidirectional-aware layered depth image, our approach synthesizes natural and realistic visuals for eye height adaptation. Quantitative and qualitative evaluation shows favorable results against state-of-the-art methods, and an extensive user study verifies improved perception and immersion for pre-captured real-world environments.

翻译：使用全向相机的预捕捉沉浸式环境为虚拟现实提供了广泛的应用场景。先前研究表明，在自我中心虚拟环境中操纵眼高可显著影响距离感知与沉浸感。然而，由于完成捕捉过程后难以改变视角，眼高在预捕捉真实环境中的影响较少受到关注。为探索这一影响，我们首先提出一项预研究：捕捉多种眼高的真实环境，并要求参与者判断自我中心距离与沉浸感。若确认存在显著影响，则需一种有效的基于图像的方法来调整预捕捉真实环境以适应使用者眼高。受该研究启发，我们提出一种基于学习的全向图像新视角合成方法，可调整眼高。该方法采用多任务架构，以两种格式学习深度与语义分割，并生成高质量的深度与语义分割以辅助修复阶段。通过改进的全向感知分层深度图像，我们的方法可合成自然逼真的视觉效果以实现眼高自适应。定量与定性评估显示该方法优于现有技术，且大规模用户研究验证了其对预捕捉真实环境感知与沉浸感的改善。