Name
Abstract | ||
|---|---|---|
Our goal is to recognize material categories using images and geometry information. In many applications, such as construction management, coarse geometry information is available. We investigate how 3D geometry (surface normals, camera intrinsic and extrinsic parameters) can be used with 2D features (texture and color) to improve material classification. We introduce a new dataset, GeoMat, which is the first to provide both image and geometry data in the form of: (i) training and testing patches that were extracted at different scales and perspectives from real world examples of each material category, and (ii) a large scale construction site scene that includes 160 images and over 800,000 hand labeled 3D points. Our results show that using 2D and 3D features both jointly and independently to model materials improves classification accuracy across multiple scales and viewing directions for both material patches and images of a large scale construction site scene. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.172 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | Volume | Issue |
Conference | abs/1607.05338 | 1 |
ISSN | Citations | PageRank |
1063-6919 | 4 | 0.41 |
References | Authors | |
25 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph DeGol | 1 | 9 | 2.27 |
| Mani Golparvar Fard | 2 | 18 | 5.30 |
| Derek Hoiem | 3 | 4998 | 302.66 |