Projects per year
We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors.Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our methodmeasures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based onsamples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration andplausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD)based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations amongobjects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.
|Number of pages||1|
|Journal||IEEE Transactions on Visualization and Computer Graphics|
|Early online date||12 Jan 2021|
|Publication status||E-pub ahead of print - 12 Jan 2021|
- 3D Indoor Scene Synthesis
- Complete Spatial Randomness
- Computational modeling
- Furniture Objects Arrangement
- Mathematical model
- Task analysis
- Three-dimensional displays
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
FingerprintDive into the research topics of 'Fast 3D Indoor Scene Synthesis by Learning Spatial Relation Priors of Objects'. Together they form a unique fingerprint.
Cosker, D., Bilzon, J., Campbell, N., Cazzola, D., Colyer, S., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., McGuigan, P., O'Neill, E., Richardt, C., Salo, A., Seminati, E., Tabor, A. & Yang, Y.
1/09/15 → 28/02/21
Project: Research council