Surface Layout Estimation Using Multiple Segmentation Methods and 3D Reasoning

摘要

In this paper we present a novel algorithm to estimate the surface layout of an indoor scene, which can serve as a visual cue for many different applications, e.g. 3D tracking, or localization in visual odometry. The main contribution of this work lies in combining multiple superpixel segmentation methods in order to obtain semantically meaningful regions. For each segmentation method, we combine 3D reasoning with semantic reasoning to generate multiple surface layout label hypotheses for each pixel. We then get the final label for each pixel within a Markov Random Field (MRF) by combining all hypothesis and by enforcing spatial consistency between neighboring pixels. Experimental results on complex indoor scenes show that our proposed method outperforms state-of-the-art methods.