DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes

Saumitro Dasgupta, Kuan Fang, Kevin Chen, Silvio Savarese; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 616-624

Abstract


We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In this paper, we present a method that uses a fully convolutional neural network (FCNN) in conjunction with a novel optimization framework for generating layout estimates. We demonstrate that our method is robust in the presence of clutter and handles a wide range of highly challenging scenes. We evaluate our method on two standard benchmarks and show that it achieves state of the art results, outperforming previous methods by a wide margin.

Related Material


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[bibtex]
@InProceedings{Dasgupta_2016_CVPR,
author = {Dasgupta, Saumitro and Fang, Kuan and Chen, Kevin and Savarese, Silvio},
title = {DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}