Visual Computing

University of Konstanz
ACM Transactions on Graphics

Quality-driven Poisson-guided Autoscanning

S. Wu, W. Sun, P. Long, H. Huang, D. Cohen-Or, M. Gong, O. Deussen, B. Chen

Abstract

We present a quality-driven, Poisson-guided autonomous scanning method. Unlike previous scan planning techniques, we do not aim to minimize the number of scans needed to cover the object’s surface, but rather to ensure the high quality scanning of the model. This goal is achieved by placing the scanner at strategically selected Next-Best-Views (NBVs) to ensure progressively capturing the geometric details of the object, until both completeness and high fidelity are reached. The technique is based on the analysis of a Poisson field and its geometric relation with an input scan. We generate a confidence map that reflects the quality/fidelity of the estimated Poisson iso-surface. The confidence map guides the generation of a viewing vector field, which is then used for computing a set of NBVs. We applied the algorithm on two different robotic platforms, a PR2 mobile robot and a one-arm industry robot. We demonstrated the advantages of our method through a number of autonomous high quality scannings of complex physical objects, as well as performance comparisons against state-of-the-art methods.

BibTeX

@article{Wu2014QualitydrivenPoisson,
  acmid      = {2661242},
  address    = {New York, NY, USA},
  articleno  = {203},
  author     = {S. Wu and W. Sun and P. Long and H. Huang and D. Cohen-Or and M. Gong and O. Deussen and B. Chen},
  doi        = {10.1145/2661229.2661242},
  issn       = {0730-0301},
  issue_date = {November 2014},
  journal    = {ACM Transactions on Graphics},
  keywords   = {3D acquisition, autonomous scanning, next-best-view},
  month      = {nov},
  number     = {6},
  numpages   = {12},
  pages      = {203:1--203:12},
  publisher  = {ACM},
  title      = {Quality-driven Poisson-guided Autoscanning},
  url        = {http://vcc.siat.ac.cn/index/getInfo?title_id=453&id=624&to_path=project},
  volume     = {33},
  year       = {2014},
}

Supplemental Material

Paper (.pdf, 24.7 MB)