Capturing Dynamic Textured Surfaces of Moving Targets

  • Ruizhe Wang
  • Lingyu Wei
  • Etienne Vouga
  • Qixing Huang
  • Duygu Ceylan
  • Gérard Medioni
  • Hao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)

Abstract

We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors. The heart of our framework is a new pairwise registration algorithm that minimizes, using a particle swarm strategy, an alignment error metric based on mutual visibility and occlusion. We show that this algorithm reliably registers partial scans with as little as 15 % overlap without requiring any initial correspondences, and outperforms alternative global registration algorithms. This registration algorithm allows us to reconstruct moving subjects from free-viewpoint video produced by consumer-grade sensors, without extensive sensor calibration, constrained capture volume, expensive arrays of cameras, or templates of the subject geometry.

Keywords

Range image registration Particle swarm optimization Dynamic surface reconstruction Free-viewpoint video Moving target Texture reconstruction 

Notes

Acknowledgments

We thank Jieqi Jiang, Xiang Ao, Jin Xu, Mingfai Wong, Bor-Jeng Chen and Anh Tran for being our capture models. This research is supported in part by Adobe, Oculus & Facebook, Sony, Pelican Imaging, Panasonic, Embodee, Huawei, the Google Faculty Research Award, The Okawa Foundation Research Grant, the Office of Naval Research (ONR)/U.S. Navy, under award number N00014-15-1-2639, the Office of the Director of National Intelligence (ODNI), and Intelligence Advanced Research Projects Activity (IARPA), under contract number 2014-14071600010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

Supplementary material

419982_1_En_17_MOESM1_ESM.mov (21 mb)
Supplementary material 1 (mov 21468 KB)
419982_1_En_17_MOESM2_ESM.pdf (2.3 mb)
Supplementary material 2 (pdf 2352 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ruizhe Wang
    • 1
  • Lingyu Wei
    • 1
  • Etienne Vouga
    • 2
  • Qixing Huang
    • 2
    • 3
  • Duygu Ceylan
    • 4
  • Gérard Medioni
    • 1
  • Hao Li
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.University of Texas at AustinAustinUSA
  3. 3.Toyota Technological Institute at ChicagoChicagoUSA
  4. 4.Adobe ResearchSan JoseUSA

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