Dominant plane recognition in interior scenes from a single image

Abstract

Recognition of dominant planes is an important task used in areas such as robot navigation, augmented reality, 3D reconstruction, among others. There are several approaches for recognizing planar structures, however, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds associated with the camera images. An alternative is to process a single image seeking to interpret areas of the images where the planar structure may be observed, thus removing parallax dependency, but adding the challenge of having to correctly interpret image ambiguities. Motivated by the latter, this work presents initial results of a novel methodology for dominant planes recognition in a single image by combining three key strategies: a learning algorithm, a segmentation scheme and a contour detection method. We constraint our approach to work with interior scenes as an attempt to identify key elements that may help in the recognition process in this sort of scenes. In this sense, our results show a recognition accuracy of 60.17% with an error of 3.14%, which indicate the feasibility of our approach.