Fast Scene Layout Estimation via Deep Hashing

In this work, we propose an efficient method for accurately estimating the scene layout in both outdoor and indoor scenarios. For outdoor scenes, the horizon line in a road image is estimated while for indoor scenes, the wall-wall, wall-ceiling and wall-floor edges are estimated. A number of image patches are first cropped from the image and then feed into a convolution neural network which is originally trained for object detection. The yielded deep features from three different layers are compared with the features of the training patches, in a spatial-aware hashing fashion. The horizon line is then estimated via a sophisticated voting stage in which different voters are considered differently according to their importances. In particular, for the more complex labels (in indoor scenes), we introduce the structural forest for further enhancing the deep features before learning the hashing function. In practice, the proposed algorithm outperforms the state-of-the-art methods in accuracy for outdoor scenes while achieves the comparable performance to the best indoor scene layout estimators. Further more, the proposed method is real-time speed (up to 25 fps).