Depth-Aware Image and Video Orientation Estimation
It addresses the need for robust orientation estimation in immersive and autonomous systems, but the improvement over existing methods is not quantified.
The paper proposes a depth-aware method for image and video orientation estimation that uses depth distribution across quadrants, depth gradient consistency, and horizontal symmetry analysis. It outperforms existing techniques in robustness and accuracy for applications like VR and autonomous navigation.
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.