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Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy.

Qi He, Guang Feng, Sophia Bano

IEEE Journal of Biomedical and Health Informatics

|July 5, 2024

Summary

This study introduces MonoLoT, a novel self-supervised monocular depth estimation framework for digestive endoscopy. MonoLoT enhances navigation and 3D reconstruction in the gastrointestinal tract, improving accuracy and generalisation.

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Area of Science:

Medical Imaging
Computer Vision
Robotics

Background:

Self-supervised monocular depth estimation is crucial for medical imaging, especially in gastrointestinal endoscopy where ground-truth depth is often unavailable.
Existing frameworks struggle with low-texture areas, limited generalisation to real-world data, and downstream applications like visual servoing.

Purpose of the Study:

To develop an improved self-supervised monocular depth estimation framework for digestive endoscopy.
To address limitations in texture-poor environments, enhance generalisation, and enable visual servoing applications.

Main Methods:

Proposed MonoLoT framework incorporating point matching loss and batch image shuffle.
Conducted extensive ablation studies on C3VD and SimCol datasets.
Integrated the method into a robotic platform for visual servoing demonstration.

Main Results:

MonoLoT achieved substantial improvements, reaching accuracies of 0.944 on C3VD and 0.959 on SimCol.
Outperformed both depth-supervised and self-supervised baselines on C3VD and real-world endoscopic data.
Successfully demonstrated real-time automatic intervention and control in digestive endoscopy via visual servoing.

Conclusions:

MonoLoT significantly advances monocular depth estimation for digestive endoscopy.
The framework overcomes key challenges, showing strong generalisation and applicability in downstream tasks.
Opens promising avenues for enhanced navigation, 3D reconstruction, and robotic control in medical applications.