Joint Color and Depth Segmentation based on Region Merging and Surface Fitting

The recent introduction of consumer depth cameras has opened the way to novel segmentation approaches exploiting depth data together with the color information. This paper proposes a region merging segmentation scheme that jointly exploits the two clues. Firstly a set of multi-dimensional vectors is built considering the 3D spatial position, the surface orientation and the color data associated to each scene sample. Normalized cuts spectral clustering is applied to the obtained vectors in order to over-segment the scene into a large number of small segments. Then an iterative merging procedure is used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm tries to combine close compatible segments and uses a NURBS surface fitting scheme on the considered segments in order to understand if the regions candidate for the merging correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation