Indoor Scene Layout Estimation from a Single Image

With the popularity of the hand devices and intelligent agents, many aimed to explore machine's potential in interacting with reality. Scene understanding, among the many facets of reality interaction, has gained much attention for its relevance in applications such as augmented reality (AR). Scene understanding can be partitioned into several sub tasks (i.e., layout estimation, scene classification, saliency prediction, etc). In this paper, we propose a deep learning-based approach for estimating the layout of a given indoor image in real-time. Our method consists of a deep fully convolutional network, a novel layout-degeneration augmentation method, and a new training pipeline which integrate an adaptive edge penalty and smoothness terms into the training process. Unlike previous deep learning-based methods that depend on post-processing refinement (e.g., proposal ranking and optimization), our method motivates the generalization ability of the network and the smoothness of estimated layout edges without deploying postprocessing techniques. Moreover, the proposed approach is time-efficient since it only takes the model one forward pass to render accurate layouts. We evaluate our method on LSUN Room Layout and Hedau dataset and obtain estimation results comparable with the state-of-the-art methods.