Name
Abstract | ||
|---|---|---|
We present a system for adaptive synthesis of indoor scenes given an empty room and only a few object categories. Automatically suggesting indoor objects and proper layouts to convert an empty room to a 3D scene is challenging, since it requires interior design knowledge to balance the factors like space, path distance, illumination and object relations, in order to insure the functional plausibility of the synthesized scenes. We exploit a database of 2D floor plans to extract object relations and provide layout examples for scene synthesis. With the labeled human positions and directions in each plan, we detect the activity relations and compute the coexistence frequency of object pairs to construct activity-associated object relation graphs. Given the input room and user-specified object categories, our system first leverages the object relation graphs and the database floor plans to suggest more potential object categories beyond the specified ones to make resulting scenes functionally complete, and then uses the similar plan references to create the layout of synthesized scenes. We show various synthesis results to demonstrate the practicability of our system, and validate its usability via a user study. We also compare our system with the state-of-the-art furniture layout and activity-centric scene representation methods, in terms of functional plausibility and user friendliness.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3130800.3130805 | ACM Trans. Graph. |
Keywords | Field | DocType |
3D scenes, adaptive scene synthesis, object relation graph | Graph,Computer vision,Usability,Exploit,Object relations theory,Artificial intelligence,Interior design,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 6 | 0730-0301 |
Citations | PageRank | References |
15 | 0.61 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Fu | 1 | 29 | 6.34 |
| Xiaowu Chen | 2 | 605 | 45.05 |
| Xiaotian Wang | 3 | 47 | 6.75 |
| Sijia Wen | 4 | 19 | 1.45 |
| Bin Zhou | 5 | 25 | 3.80 |
| Hongbo Fu | 6 | 1167 | 73.64 |