A Survey of Surface Reconstruction from Point Clouds

Matthew Berger (1) , Andrea Tagliasacchi (2) , Lee Seversky (1) , Pierre Alliez (3) , Gael Guennebaud (4) , Joshua Levine (5) , Andrei Sharf (6) , Claudio Silva (7)

1 AFRL - Air Force Research Laboratory
2 UVIC - University of Victoria [Canada]
3 TITANE - Geometric Modeling of 3D Environments
4 MANAO - Melting the frontiers between Light, Shape and Matter
5 School of Computing
6 Computer Science Department
7 Department of Computer Science and Engineerin [New York]

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. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction.

Domaines: Géométrie algorithmique [cs.CG]

Matthew Berger, Andrea Tagliasacchi, Lee Seversky, Pierre Alliez, Gael Guennebaud, et al.. A Survey of Surface Reconstruction from Point Clouds. Computer Graphics Forum, 2016, pp.27. ⟨10.1111/cgf.12802⟩. ⟨hal-01348404v1⟩