Efficient Small-Scale Network for Room Layout Estimation

Abstract

Retrieving the layout of different cluttered indoor scenes from monocular images is a challenging task, using a smaller deep neural network than the existing proposals. Previous geometric solutions are prone to failure in the presence of cluttered scenes because they depend strongly on hand-engineering features and the expectation of the possibility of the vanishing points’ calculation. With the growth of neural networks, the geometric methods were either replaced or fused within the emerging area of deep learning. The more recent solutions rely on dense neural networks with additional adjustments, either by the calculation of the vanishing points, position based, or by layout ranking. All these methods presented valid solutions to this challenge with the flaw of being computationally demanding. Here, we present a more lightweight solution, running the segmentation on a smaller neural network and introducing a discriminative classifier for the posterior layout ranking and optimization. Our proposed method is evaluated by two standard dataset benchmarks, achieving near state of the art results even with a fraction of the required parameters than the available state of the art methods.