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Cylinders in Three-Dimensional Space01:28

Cylinders in Three-Dimensional Space

A cylindrical surface is generated when a two-dimensional profile curve is translated along a straight line in three-dimensional space. The translated copies of the curve form a surface composed of parallel rulings, each oriented in the same fixed direction. This construction allows many three-dimensional forms to be described using relatively simple planar equations.In Cartesian coordinates, a cylindrical surface is often recognized by an equation that omits one of the three variables. For...

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Related Experiment Video

Updated: Jul 8, 2026

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure
10:22

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

3D modeling of optically challenging objects.

Johnny Park1, Avinash Kak

  • 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907-2035, USA. jpark@purdue.edu

IEEE Transactions on Visualization and Computer Graphics
|January 15, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces multi-peak range imaging to create accurate 3D models of difficult surfaces. The new system effectively removes errors, improving 3D reconstruction for optically challenging objects.

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Optical Sensing

Background:

  • Optically challenging surfaces (e.g., shiny, transparent) hinder accurate 3D model creation.
  • Traditional range sensing methods struggle with erroneous data from such surfaces.

Purpose of the Study:

  • To develop a robust system for 3D model construction of objects with optically challenging surfaces.
  • To improve the reliability and accuracy of 3D reconstruction in challenging scenarios.

Main Methods:

  • Utilized a novel multi-peak range imaging concept to capture multiple range measurements per point.
  • Applied a series of constraint tests (local and global) to eliminate erroneous data caused by surface properties and reflections.
  • Acquired ground truth data by modifying object surfaces to evaluate constraint test performance.

Main Results:

  • Successfully constructed 3D models for five different optically challenging objects.
  • Demonstrated significant improvement over traditional methods in generating reliable 3D models.
  • Evaluated the effectiveness of constraint tests and parameter effects using ground truth data.

Conclusions:

  • The proposed multi-peak range imaging system with constraint tests effectively addresses challenges in 3D reconstruction.
  • This method enhances the reliability of 3D models for objects with difficult optical properties.
  • The system offers a significant advancement for 3D scanning applications involving optically complex surfaces.