Journal
ACM TRANSACTIONS ON GRAPHICS
Volume 37, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3197517.3201362
Keywords
scene synthesis; object layout; neural networks; convolutional networks; deep learning
Categories
Funding
- Intel
- Technical University of Munich-Institute for Advanced Study - German Excellence Initiative
- European Union Seventh Framework Programme [291763]
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We present a convolutional neural network based approach for indoor scene synthesis. By representing 3D scenes with a semantically-enriched image-based representation based on orthographic top-down views, we learn convolutional object placement priors from the entire context of a room. Our approach iteratively generates rooms from scratch, given only the room architecture as input. Through a series of perceptual studies we compare the plausibility of scenes generated using our method against baselines for object selection and object arrangement, as well as scenes modeled by people. We find that our method generates scenes that are preferred over the baselines, and in some cases are equally preferred to human-created scenes.
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