Depth-Aware Image and Video Orientation Estimation

TL;DR

This paper presents a depth-based method for image and video orientation estimation that improves robustness and accuracy by using depth distribution, gradient consistency, and symmetry analysis.

Contribution

It introduces a novel hybrid approach leveraging depth cues, depth gradient consistency, and symmetry analysis for more accurate orientation estimation.

Findings

Outperforms existing techniques in diverse scenarios

Provides robust and accurate orientation correction

Enhances perceptual stability in immersive content

Abstract

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.