Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Understand

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.