Scene Synthesis via Uncertainty-Driven Attribute Synchronization
Haitao Yang, Zaiwei Zhang, Siming Yan, Haibin Huang, Chongyang Ma, Yi, Zheng, Chandrajit Bajaj, Qixing Huang

TL;DR
This paper presents a neural scene synthesis method that captures diverse 3D scene features by combining neural networks with probabilistic priors, enabling the generation of realistic and consistent scenes.
Contribution
It introduces a novel approach that integrates uncertainty-driven attribute synchronization with neural networks for improved 3D scene synthesis.
Findings
Outperforms existing scene synthesis methods
Faithfully interpolates training data with diverse feature patterns
Effectively enforces consistency constraints among attributes
Abstract
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…
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Taxonomy
Topics3D Surveying and Cultural Heritage · Image Processing and 3D Reconstruction · Computer Graphics and Visualization Techniques
