Name
Abstract | ||
|---|---|---|
We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF. We assume that multiple images of the object are obtained under fixed view-point and varying illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary, we derive a per-pixel surface normal and BRDF estimation framework that requires neither iterative optimization techniques nor careful initialization, both of which are endemic to most state-of-the-art techniques. We showcase the performance of our technique on a wide range of simulated and real scenes where we outperform competing methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCPHOT.2015.7168363 | ICCP |
Keywords | Field | DocType |
Photometric stereo, BRDF estimation, Dictionaries, Spatially varying BRDF | Bidirectional reflectance distribution function,Computer vision,Computer science,Artificial intelligence,Pixel,Initialization,Reflectivity,Normal,Photometric stereo | Journal |
Volume | ISSN | Citations |
abs/1503.04265 | 2164-9774 | 4 |
PageRank | References | Authors |
0.40 | 23 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Zhuo | 1 | 34 | 4.94 |
| Aswin C. Sankaranarayanan | 2 | 770 | 51.51 |