Scene segmentation from depth and color data driven by surface fitting

Scene segmentation is a very challenging problem for which color information alone is often not sufficient. Recently the introduction of consumer depth cameras has opened the way to novel approaches exploiting depth data. This paper proposes a novel segmentation scheme that exploits the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color and geometry information and normalized cuts spectral clustering is applied to them in order to coarsely segment the scene. Then a NURBS model is fitted on each of the computed segments. The accuracy of the fitting is used as a measure of the plausibility that the segment represents a single surface or object. Segments that do not represent a single surface are split again into smaller regions and the process is iterated until the optimal segmentation is obtained. Experimental results show how the proposed method allows to obtain an accurate and reliable scene segmentation.