利用深度分布估算图像与视频方向，提升虚拟现实等场景的感知稳定性。
Depth-Aware Image and Video Orientation Estimation
- 基于图像各象限的深度分布判断方向
- 结合深度梯度一致性和水平对称性分析，实现精准校正
- 适合VR/AR、自动驾驶等需要空间感知的场景
本文提出一种新型图像与视频方向估计方法，通过利用自然图像中的深度分布实现方向判断。该方法基于图像不同象限的深度分布特征进行方向推断，构建了一个适用于虚拟现实（VR）、增强现实（AR）、自主导航和交互式监控系统等场景的鲁棒框架。为进一步提升细粒度感知对齐效果，引入深度梯度一致性（DGC）与水平对称性分析（HSA），有效利用深度线索支持沉浸式视觉内容的空间连贯性与感知稳定性。定性与定量评估表明，所提方法在多种场景下均优于现有技术，具备更强的鲁棒性与准确性。
原文摘要 · Abstract (English)
This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed method estimates the orientation based on the depth distribution across different quadrants of the image, providing a robust framework for orientation estimation suited for applications such as virtual reality (VR), augmented reality (AR), autonomous navigation, and interactive surveillance systems. To further enhance fine-scale perceptual alignment, we incorporate depth gradient consistency (DGC) and horizontal symmetry analysis (HSA), enabling precise orientation correction. This hybrid strategy effectively exploits depth cues to support spatial coherence and perceptual stability in immersive visual content. Qualitative and quantitative evaluations demonstrate the robustness and accuracy of the proposed approach, outperforming existing techniques across diverse scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。