Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predict their category, location, orientation and size with s...

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Bibliographic Details
Published in2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 6175 - 6183
Main Authors Ritchie, Daniel, Wang, Kai, Lin, Yu-An
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Computational modeling
Computer vision
Deep Learning
Neural networks
Pipelines
Scene Analysis and Understanding
Training
Training data
Vision + Graphics
Visualization
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Summary:We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predict their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes, without any modifications. Our method is significantly faster than the previous image-based method, and generates results that outperforms it and other state-of-the-art deep generative scene models in terms of faithfulness to training data and perceived visual quality.
ISSN:2575-7075
DOI:10.1109/CVPR.2019.00634