Name
Abstract | ||
|---|---|---|
This paper presents a new method for 3D model labeling guided by weakly tagged 2D color images. Many previous methods on 3D model labeling achieve impressive performances using large training data sets. However, it is difficult and time-consuming to build such a carefully annotated data set. In order to solve this problem, we take advantage of the large number of weakly tagged color images to label the 3D models. In our approach, we first collect and tag the web color images with semantic annotations. Then we project the input 3D model into multiview projections. Through the multiview alignment, we transfer the semantic labels onto the model projections via a color-weighting process. Combining pre-segment information, we back-project the labels and get final labeling results. Experimental results between two benchmarks show that our approach could get comparable labeling accuracy compared to other two state-of-art methods without expensive training cost. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.gmod.2018.02.001 | Graphical Models |
Keywords | Field | DocType |
3D model labeling,Image-guided,Multiview alignment | Computer vision,Artificial intelligence,Final Labeling,Training data sets,Mathematics | Journal |
Volume | ISSN | Citations |
96 | 1524-0703 | 0 |
PageRank | References | Authors |
0.34 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kan Guo | 1 | 98 | 4.74 |
| Xiaowu Chen | 2 | 605 | 45.05 |
| Bin Zhou | 3 | 74 | 5.45 |
| Qinping Zhao | 4 | 363 | 43.20 |