STAR Presentation Slide


Eurographics STAR (Proc. of EG'14)



The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. This state-of-the-art report surveys the field of surface reconstruction, providing a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, this report provides a detailed characterization of the field, highlights similarities between diverse reconstruction techniques, and provides directions for future work in surface reconstruction.


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Paper ([PDF]() ~15 MB)
Slides ([Zip]() ~250MB, individual chapters below ~50MB each)

  1. Introduction ([PDF]())
  2. Surface smoothness priors ([PDF](), [Key09](), [MOV]())
  3. Low level priors ([PDF]())
  4. High level priors ([PDF]())
  5. Conclusions ([PDF]())

Bibtex Reference

   title = {State of the Art in Surface Reconstruction from Point Clouds}, 
   author = {Matthew Berger and Andrea Tagliasacchi and Lee M. Seversky and Pierre Alliez and 
             Joshua A. Levine and Andrei Sharf and Claudio Silva}, 
   journal = {Eurographics STAR (Proc. of EG'14) }, 
   year = 2014}


We would like to thank Misha Kazhdan, Mark Pauly, Mario Botsch, Yaron Lipman, Florent Lafarge, Gael Guennebaud, Oliver Mattausch and Alexandre Sorkine-Hornung as well as the reviewers for their valuable feedback.