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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

550
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.
550

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Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera.

Hanbit Gil1, Sehyun Ryu1, Sungmin Woo1

  • 1Department of Information and Communication Engineering, Korea University of Technology and Education (KOREATECH), Cheonan-si 31253, Republic of Korea.

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|December 17, 2024
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Summary
This summary is machine-generated.

This study enhances Semi-Global Matching (SGM) disparity maps using Maximum a Posteriori (MAP) estimation. The improved maps boost neural network performance for depth sensing in autonomous systems.

Keywords:
MAP estimationSemi-Global Matchingautonomous drivingdeep learningdisparity mapinterpolationneural networkstereo vision

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Semi-Global Matching (SGM) provides accurate disparity maps but struggles with occlusions and textureless regions.
  • Neural networks offer visually appealing depth estimation but face generalization issues.
  • Accurate ground truth disparity maps are crucial for training depth-sensing systems.

Purpose of the Study:

  • To enhance ground truth disparity maps generated by SGM.
  • To improve the visual quality and usability of SGM disparity maps for training neural networks.
  • To address limitations of SGM in occluded and textureless areas.

Main Methods:

  • Utilized Maximum a Posteriori (MAP) estimation to refine SGM disparity maps.
  • Employed Bayesian inference and interpolation of surrounding disparity information to correct invalid pixels.
  • Developed a novel method to enhance existing SGM outputs.

Main Results:

  • The enhanced disparity maps maintained SGM's accuracy in valid regions.
  • Improved visual quality and reduced invalid disparity values compared to standard SGM.
  • Significantly boosted the performance of neural network-based depth estimation models.

Conclusions:

  • The proposed MAP-based enhancement method offers a robust solution for improving disparity map accuracy.
  • This technique enhances the utility of SGM for training commercial depth-sensing devices and autonomous applications.
  • The enhanced maps provide a reliable foundation for advanced stereoscopic vision systems.