# Depth-Aware Image and Video Orientation Estimation

arXiv: https://arxiv.org/abs/2604.13995 · HTML: https://mlsanity.com/paper/2604.13995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [iDisc: Internal Discretization for Monocular Depth Estimation](https://mlsanity.com/paper/2304.06334/) (2023)
- [Robust Monocular Depth Estimation under Challenging Conditions](https://mlsanity.com/paper/2308.09711/) (2023)
- [Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range](https://mlsanity.com/paper/1611.05008/) (2016)
- [Deep Image Orientation Angle Detection](https://mlsanity.com/paper/2007.06709/) (2020)
- [Automatic Photo Orientation Detection with Convolutional Neural Networks](https://mlsanity.com/paper/2305.10319/) (2023)
- [Image and Depth from a Single Defocused Image Using Coded Aperture Photography](https://mlsanity.com/paper/1603.04046/) (2016)

## Built on by (most cited first, 1 of 1)

- [Saturation-Aware Space-Variant Blind Image Deblurring](https://mlsanity.com/paper/2604.16200/) (2026)

## Abstract (arXiv)

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.