Automatic Mechanism Modeling from a Single Image with CNNs
| dc.contributor.author | Lin, Minmin | en_US |
| dc.contributor.author | Shao, Tianjia | en_US |
| dc.contributor.author | Zheng, Youyi | en_US |
| dc.contributor.author | Ren, Zhong | en_US |
| dc.contributor.author | Weng, Yanlin | en_US |
| dc.contributor.author | Yang, Yin | en_US |
| dc.contributor.editor | Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes | en_US |
| dc.date.accessioned | 2018-10-07T14:59:57Z | |
| dc.date.available | 2018-10-07T14:59:57Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high-quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter-part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high-quality 3D mechanism models without user guidance. | en_US |
| dc.description.number | 7 | |
| dc.description.sectionheaders | 3D Modeling | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13572 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 337-348 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13572 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13572 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Image processing | |
| dc.subject | Shape modeling | |
| dc.subject | Neural networks | |
| dc.title | Automatic Mechanism Modeling from a Single Image with CNNs | en_US |