  We present a fast framework for indoor scene synthesis, given a room geometry
and a list of objects with learnt priors. Unlike existing data-driven
solutions, which often extract priors by co-occurrence analysis and statistical
model fitting, our method measures the strengths of spatial relations by tests
for complete spatial randomness (CSR), and extracts complex priors based on
samples with the ability to accurately represent discrete layout patterns. With
the extracted priors, our method achieves both acceleration and plausibility by
partitioning input objects into disjoint groups, followed by layout
optimization based on the Hausdorff metric. Extensive experiments show that our
framework is capable of measuring more reasonable relations among objects and
simultaneously generating varied arrangements in seconds.
