Physics Inspired Optimization on Semantic Transfer Features: An Alternative
Method for Room Layout Estimation
Abstract
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method
enjoys the benefits of two novel techniques. The first one
is semantic transfer (ST), which is: (1) a formulation to
integrate the relationship between scene clutter and room
layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances,
and in order to address the computation redundance hidden
in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO).
PIO’s basic idea is to formulate some phenomena observed
in ST features into mechanics concepts. Evaluations on
public datasets LSUN and Hedau show that the proposed
method is more accurate than state-of-the-art methods