Name
Title | ||
|---|---|---|
Fast Mesh Denoising With Data Driven Normal Filtering Using Deep Variational Autoencoders |
Abstract | ||
|---|---|---|
Recent advances in 3-D scanning technology have enabled the deployment of 3-D models in various industrial applications such as digital twins, remote inspection, and reverse engineering. Despite their evolving performance, 3-D scanners still introduce noise and artifacts in the acquired dense models. In this article, we propose a fast and robust denoising method for the dense 3-D scanned industrial models. The proposed approach employs conditional variational autoencoders to effectively filter face normals. Training and inference are performed in a sliding patch setup reducing the size of the required training data and execution times. We conducted extensive evaluation studies using 3-D scanned and CAD models. The results verify plausible denoising outcomes, demonstrating similar or higher reconstruction accuracy, compared to other state-of-the-art approaches. Specifically, for 3-D models with more than 1e4 faces, the presented pipeline is twice as fast as methods with equivalent reconstruction error. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2020.3000491 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
3-D mesh denoising,data driven normal filtering,variational autoencoders | Journal | 17 |
Issue | ISSN | Citations |
2 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stavros Nousias | 1 | 7 | 3.71 |
| Gerasimos Arvanitis | 2 | 9 | 6.21 |
| Aris S. Lalos | 3 | 192 | 32.84 |
| K. Moustakas | 4 | 285 | 58.02 |