A Generative Model of 3D Object Layouts in Apartments
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Understanding indoor scenes is an important task in computer vision. This task is typically ambiguous, so we require a strong prior, that captures the regularity of indoor environments. This is naturally expressed by a probabilistic model over 3D room layouts and geometry, reasoning over complex layouts in 3D space, including high-order spatial relations among objects. In this work, we construct such a model, trained on over 250000 human-designed rooms with 170 object classes. We conduct extensive experiments to show the quality of our model. First, we show that it generates samples that are plausible, by an extensive user study involving human comparisons of sampled layouts to ground-truth. Second, we demonstrate the value of incorporating spatial relationships between objects, by showing that this increases the plausibility of samples. Third, we show that our model generalises, rather than simply memorising its training set. Finally, we provide many examples of knowledge learnt by our model, such as support relationships, and common spatial relations between object classes.
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