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Photorealistic Learned Landscapes for Augmented Reality
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Published on: June 27, 2025

Visibility in three-dimensional cluttered scenes.

Michael S Langer1, Fahim Mannan

  • 1School of Computer Science, McGill University, Montreal, Canada. michael.langer@mcgill.ca

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|December 4, 2012
PubMed
Summary
This summary is machine-generated.

Understanding surface visibility in 3D cluttered scenes is key for computer vision. This study models how surface visibility probabilities depend on scene parameters, aiding object recognition and depth perception in complex environments.

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Area of Science:

  • Computer Vision
  • 3D Scene Understanding
  • Computational Geometry

Background:

  • Three-dimensional (3D) cluttered scenes, like foliage, present significant challenges for computer vision tasks.
  • Partial visibility of surfaces and objects hinders accurate object recognition and depth perception.

Purpose of the Study:

  • To investigate and model the probabilities of surface visibility within 3D cluttered scenes.
  • To understand how scene parameters influence the visibility of gaps, discontinuities, and occluded points.

Main Methods:

  • Developed probability models for surface visibility based on scene parameters.
  • Incorporated factors like surface size, density, depth, and inverse depth into the models.
  • Utilized synthetic 3D cluttered scenes generated via computer graphics for model verification.

Main Results:

  • Quantified the relationship between scene parameters and the likelihood of visible gaps, depth discontinuities, and occluded points.
  • Demonstrated the importance of inverse depth for predicting binocular disparity and motion parallax.
  • Validated the probability models using simulated 3D environments.

Conclusions:

  • The developed models provide a framework for analyzing surface visibility in complex 3D environments.
  • Findings contribute to improving algorithms for object recognition and depth estimation in cluttered scenes.
  • Understanding visibility probabilities is crucial for advancing 3D vision capabilities.