Name
Abstract | ||
|---|---|---|
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 contain 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1111/cgf.12802 | Comput. Graph. Forum |
Keywords | Field | DocType |
geometry processin,surface reconstruction,3D acquisition,shape analysis | Categorization,Surface reconstruction,Computer vision,Physical shape,Computer science,Artificial intelligence,Point cloud,Prior probability,Shape analysis (digital geometry) | Journal |
Volume | Issue | ISSN |
36 | 1 | 0167-7055 |
Citations | PageRank | References |
50 | 1.32 | 128 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Berger | 1 | 112 | 5.17 |
| Andrea Tagliasacchi | 2 | 716 | 31.90 |
| Lee M. Seversky | 3 | 150 | 9.08 |
| Pierre Alliez | 4 | 2749 | 135.44 |
| Gaël Guennebaud | 5 | 702 | 28.95 |
| Joshua A. Levine | 6 | 369 | 19.64 |
| Andrei Sharf | 7 | 1108 | 49.93 |
| Cláudio T. Silva | 8 | 5054 | 290.90 |