Depth-Aware Image and Video Orientation Estimation
Muhammad Z. Alam, Larry Stetsiuk, M. Umair Mukati, Zeeshan Kaleem
TLDR
A novel method uses depth distribution and gradient consistency to robustly estimate image/video orientation for VR/AR applications.
Key contributions
- Introduces a novel depth-aware method for robust image and video orientation estimation.
- Leverages depth distribution across image quadrants for accurate orientation sensing.
- Enhances precision using Depth Gradient Consistency (DGC) and Horizontal Symmetry Analysis (HSA).
- Outperforms existing techniques, suitable for VR, AR, and autonomous navigation.
Why it matters
Accurate image and video orientation is critical for immersive technologies like VR/AR and autonomous systems. This paper offers a robust, depth-aware solution that significantly improves perceptual stability and spatial coherence, addressing a key challenge in these fields.
Original 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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.