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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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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a

Ivan Y Alba Corpus1,2, Wendy Flores-Fuentes1, Oleg Sergiyenko3

  • 1Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study demonstrates that a Technical Vision System (TVS) can create recognizable 3D scans with minimal data points. Advanced filtering and regression models reduce noise and distortion for detailed 3D reconstruction.

Keywords:
3D reconstructioncloud registrationspatial coordinate cloud

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Area of Science:

  • Computer Vision
  • Metrology
  • 3D Scanning Technologies

Background:

  • Technical Vision Systems (TVS) integrate laser scanning and sensors for 3D spatial coordinate measurement.
  • Exploring TVS for 3D scanning at reduced resolutions presents challenges in data quality and processing time.

Purpose of the Study:

  • To investigate the potential of a TVS for efficient 3D object digitalization at lower resolutions.
  • To develop methods for generating recognizable 3D scans using minimal 3D points, balancing scan time and data volume.

Main Methods:

  • Utilized a rotating table for object scanning with a TVS.
  • Employed statistical data filtering and regression models to optimize scanning windows and reduce noise.
  • Applied 3D point registration and alignment to ground truth models for evaluation.

Main Results:

  • Successfully generated recognizable 3D scans from coarsely scanned or noisy data.
  • Demonstrated that detailed 3D models can be reconstructed despite initial data limitations.
  • Identified optimal scanning parameters and filtering techniques for improved scan quality.

Conclusions:

  • The TVS is capable of reconstructing sufficiently detailed 3D models, even with low-definition or noisy input data.
  • The developed methods effectively mitigate noise and distortion inherent to the TVS.
  • This approach offers a viable solution for efficient 3D scanning with reduced data requirements.