Depth-Aware Image and Video Orientation Estimation

2026-04-15

AI Summary

This work addresses the challenge of inaccurate orientation estimation in virtual and augmented reality caused by insufficient depth perception. To overcome this limitation, the authors propose a novel method that integrates depth distribution and symmetry cues to infer spatial orientation. By analyzing depth distributions across image quadrants and incorporating Depth Gradient Consistency (DGC) with Horizontal Symmetry Analysis (HSA), they construct a depth-based spatial orientation model. This approach is the first to jointly leverage depth distribution and symmetry information for orientation estimation, significantly enhancing spatial coherence and perceptual stability in immersive visual content. Experimental results demonstrate consistent superiority over existing methods across diverse scenarios, with both qualitative and quantitative evaluations confirming its robustness and high accuracy.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• orientation estimation
• depth distribution
• image orientation
• video orientation
• spatial coherence

Innovation

• depth distribution
• orientation estimation
• depth gradient consistency
• horizontal symmetry analysis
• spatial coherence