Name
Abstract | ||
|---|---|---|
Small object arrangement is very important for creating detailed and realistic 3D indoor scenes. In this article, we present an interactive framework based on active learning to help users create customized arrangements for small objects according to their preferences. To achieve this with minimal user effort, we first learn the prior knowledge about small object arrangement from a 3D indoor scene dataset through a probability mining method, which forms the initial guidance for arranging small objects. Then, users are able to express their preferences on a few small object categories, which are automatically propagated to all the other categories via a novel active learning approach. In the propagation process, we introduce a novel metric to obtain the propagation weights, which measures the degree of interchangeability between two small object categories, and is calculated based on a spatial embedding model learned from the small object neighborhood information extracted from the 3D indoor scene dataset. Experiments show that our framework is able to help users effectively create customized small object arrangements with little effort. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TVCG.2019.2949295 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | DocType | Volume |
3D object layout,active learning,scene enrichment,computer-aided aesthetic design,human computer interaction | Journal | 27 |
Issue | ISSN | Citations |
4 | 1077-2626 | 0 |
PageRank | References | Authors |
0.34 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suiyun Zhang | 1 | 9 | 1.72 |
| Han Zhizhong | 2 | 198 | 18.28 |
| Yu-Kun Lai | 3 | 1025 | 80.48 |
| Zwicker Matthias | 4 | 2513 | 129.25 |
| Hui Zhang | 5 | 188 | 24.25 |