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Related Experiment Video

Updated: Jun 16, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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SHADeS: self-supervised monocular depth estimation through non-Lambertian image decomposition.

Rema Daher¹, Francisco Vasconcelos², Danail Stoyanov²

¹Department of Computer Science, UCL Hawkes Institute, University College London, Gower Street, London, WC1E 6BT, UK. rema.daher.20@ucl.ac.uk.

International Journal of Computer Assisted Radiology and Surgery

|May 13, 2025

Summary

This summary is machine-generated.

This study introduces a new self-supervised model (SHADeS) for 3D colonoscopy scene reconstruction. By modeling specular reflections, it improves depth estimation and light decomposition, aiding navigation and polyp characterization.

Keywords:

Monocular depth Self-supervision Specular highlights

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Area of Science:

Computer Vision
Medical Imaging
Computational Geometry

Background:

3D scene reconstruction in colonoscopy aids navigation and polyp analysis.
Illumination variations, especially specular reflections, pose significant challenges.
Accurate depth and shape characterization are crucial for effective colonoscopy.

Purpose of the Study:

To investigate methods for decoupling light and depth in colonoscopy imaging.
To develop a model robust to complex illumination and specular reflections.
To improve 3D scene reconstruction for enhanced colonoscopy navigation and polyp characterization.

Main Methods:

Introduced a self-supervised model named SHADeS (Shading, Albedo, Depth, and Specularities).
SHADeS simultaneously estimates shading, albedo, depth, and specularities from single colonoscopy images.
Employed a non-Lambertian model treating specular reflections as a distinct light component, unlike prior methods.

Main Results:

Demonstrated that previous light decomposition (IID) and depth estimation (MonoViT, ModoDepth2) models are negatively impacted by specularities.
SHADeS successfully produces robust light decomposition and depth maps, unaffected by specular regions.
Quantitative comparisons on phantom data (C3VD) further validated the model's robustness.

Conclusions:

Modeling specular reflections significantly enhances depth estimation accuracy in colonoscopy.
The proposed self-supervised SHADeS approach effectively integrates light decomposition and depth estimation.
Improved light decomposition shows potential for applications like colon place recognition.