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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
08:04

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Published on: December 5, 2013

A point-cloud-based multiview stereo algorithm for free-viewpoint video.

Yebin Liu1, Qionghai Dai, Wenli Xu

  • 1Tsinghua University, Beijing, People's Republic of China. liuyebin@tsinghua.edu.cn

IEEE Transactions on Visualization and Computer Graphics
|March 13, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a robust multiview stereo (MVS) algorithm for generating 3D models from images. The point-cloud-based method excels in accuracy and completeness, even with limited viewpoints.

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Computer Graphics

Background:

  • Multiview stereo (MVS) algorithms are crucial for 3D reconstruction.
  • Existing MVS methods face challenges with noise, occlusion, and sparse data.
  • Accurate and complete 3D model generation remains a significant research problem.

Purpose of the Study:

  • To develop a robust MVS algorithm for free-viewpoint video.
  • To enhance 3D model reconstruction accuracy and completeness.
  • To address challenges posed by noise, occlusion, and lack of texture in MVS data.

Main Methods:

  • A point-cloud-based MVS scheme involving point cloud extraction, merging, and meshing.
  • Utilizing a stereo matching metric robust to common MVS data issues.
  • Integrating visual hull, frontier, and implicit points with point fidelity information.

Main Results:

  • The proposed MVS algorithm demonstrates robust performance in reconstruction.
  • Achieved competitive results compared to existing algorithms.
  • Successfully reconstructed accurate and complete 3D models from static and motion MVS datasets.

Conclusions:

  • The developed MVS algorithm offers a robust solution for free-viewpoint video.
  • The method effectively handles challenging MVS data, ensuring high-quality 3D models.
  • This approach provides a competitive edge in sparse viewpoint 3D reconstruction.