Name
Abstract | ||
|---|---|---|
We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian effects such as shadows and specularities. Unlike previous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface normals in the presence of significant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-19318-7_55 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
surface normal,correct low-rank matrix,low-rank matrix,uncalibrated photometric stereo,photometric stereo method,photometric stereo problem,new approach,missing entry,robust photometric stereo,convex optimization technique,low-rank matrix completion,new technique | Computer vision,Heuristic,Matrix completion,Computer science,Angular error,Matrix (mathematics),Matrix norm,Low-rank approximation,Artificial intelligence,Convex optimization,Photometric stereo | Conference |
Volume | Issue | ISSN |
6494 LNCS | PART 3 | 16113349 |
Citations | PageRank | References |
103 | 2.42 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lun Wu | 1 | 107 | 3.68 |
| Arvind Ganesh | 2 | 4904 | 153.80 |
| Boxin Shi | 3 | 381 | 45.76 |
| Yasuyuki Matsushita | 4 | 2046 | 113.32 |
| Yongtian Wang | 5 | 103 | 2.42 |
| Yongtian Wang | 6 | 456 | 73.00 |