  Developing deep neural networks to generate 3D scenes is a fundamental
problem in neural synthesis with immediate applications in architectural CAD,
computer graphics, as well as in generating virtual robot training
environments. This task is challenging because 3D scenes exhibit diverse
patterns, ranging from continuous ones, such as object sizes and the relative
poses between pairs of shapes, to discrete patterns, such as occurrence and
co-occurrence of objects with symmetrical relationships. This paper introduces
a novel neural scene synthesis approach that can capture diverse feature
patterns of 3D scenes. Our method combines the strength of both neural
network-based and conventional scene synthesis approaches. We use the
parametric prior distributions learned from training data, which provide
uncertainties of object attributes and relative attributes, to regularize the
outputs of feed-forward neural models. Moreover, instead of merely predicting a
scene layout, our approach predicts an over-complete set of attributes. This
methodology allows us to utilize the underlying consistency constraints among
the predicted attributes to prune infeasible predictions. Experimental results
show that our approach outperforms existing methods considerably. The generated
3D scenes interpolate the training data faithfully while preserving both
continuous and discrete feature patterns.
