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Related Concept Videos

Depth Perception and Spatial Vision01:15

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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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Related Experiment Video

Updated: Apr 15, 2026

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Enhanced distance perception in automated robotic endoscopy through scale-aware monocular depth estimation.

Ruofeng Wei1, Kai Chen1, Yiyao Ma1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China.

International Journal of Computer Assisted Radiology and Surgery
|April 14, 2026
PubMed
Summary

This study introduces a new monocular image-based system for accurate distance perception in robotic surgery. The framework enhances surgical robot navigation and safety in minimally invasive procedures by providing precise depth estimation from endoscopic views.

Keywords:
Distance perceptionEndoscopic robotic surgeryGeometric modelingScale-aware monocular depth estimation

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Area of Science:

  • Robotics
  • Computer Vision
  • Surgical Technology

Background:

  • Accurate distance perception is critical for safe and effective robotic surgery, especially in minimally invasive procedures.
  • Current distance estimation methods often rely on expensive or ineffective sensors in endoscopic environments.
  • Limited visual feedback in endoscopic surgery necessitates advanced perception capabilities for surgical robots.

Purpose of the Study:

  • To develop a monocular image-based framework for accurate distance measurements in surgical robotics.
  • To enhance the autonomous navigation capabilities of surgical robots in complex anatomical structures.
  • To overcome the limitations of existing distance estimation methods in endoscopic settings.

Main Methods:

  • A monocular endoscopic image-based pipeline for scale-aware depth estimation.
  • Utilizing surgical instruments with known cylindrical geometry for absolute scale recovery.
  • An automatic module for continuous tracking of tissues and instruments for precise distance calculations.

Main Results:

  • Reduced mean absolute error from 8.56 mm to 6.35 mm compared to baselines.
  • Improved depth estimation accuracy from 76.9% to 88.0% with near-perfect absolute scale.
  • Achieved average distance errors between 4.7 mm and 6.4 mm in complex endoscopic scenarios.

Conclusions:

  • A novel image-based distance perception framework enhances surgical robot automation.
  • The framework enables efficient object distance measurements within endoscopic views.
  • Demonstrated potential to benefit robotic surgery tasks, including automatic navigation.