Scene Segmentation Driven by Deep Learning and Surface Fitting

This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples 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

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on 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, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation

Ground plane detection using an RGB-D sensor

Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a very simple but robust ground plane detection method based on depth information obtained using anRGB-Depth sensor. We present two different variations of the method: the simplest one is robust in setups where the sensor pitch angle is fixed and has no roll, whereas the second one can handle changes in pitch and roll angles. Our comparisons show that our approach performs better than the vertical disparity approach. It produces accurate ground plane-obstacle segmentation for difficult scenes, which include many obstacles, different floor surfaces, stairs, and narrow corridors.Publisher's VersionAuthor Post Prin

Segmentation of color and depth data based on surface fitting

This thesis presents novel iterative schemes for the segmentation of scenes acquired by RGB-D sensors. Both the problems of objects segmentation and of semantic segmentation (labeling) are considered. The first building block of the proposed methods is the Normalized Cuts algorithm, based on graph theory and spectral clustering techniques, that provides a segmentation exploiting both geometry and color information. A limitation is the fact that the number of segments (equivalently, the number of objects in the scene) must either be decided in advance, or requires an arbitrary threshold on the normalized cut measure to be controlled. In addition, this method tends to provide segments of similar size, while in many real world scenes the dimensions of the objects and structures are widely variable. To overcome these drawbacks, we present iterative schemes based on the approximation with parametric NURBS surfaces (Non-Uniform Rational B-Splines). The key idea is to consider the results of the surface fitting as an estimation of how good the current segmentation is. This makes it possible to build region splitting and region merging procedures, in which the fitting results are compared at each step against the previous ones, and the iterations are moved forward based on whether they turn out to be improved or not, until an optimal final solution is reached. The rationale is that, if a segment properly corresponds to an actual object in the scene, the fitting result is expected to be good, while segments that need to be subdivided or merged with other ones are expected to give a larger error. A discussion of several possible metrics to evaluate the quality of the surface fitting is presented. In all the presented schemes, the employment of NURBS surfaces approximation is a novel contribution. Subsequently, it is described how the proposed iterative schemes can be coupled with a Deep Learning classification step performed with CNNs (Convolutional Neural Networks), by introducing a measure of similarity between the elements of an initial over-segmentation. This information is used together with the surface fitting results to control the steps of a revised iterative region merging procedure. In addition, some information (fitting error, surface curvatures) resulting from the NURBS fitting on the initial over-segmentation is fed into the Convolutional Neural Networks themselves. To the best of our knowledge, this is the first work where this kind of information is used within a Deep Learning framework. Finally, the objects segmentation resulting from the region merging procedure is exploited to effectively improve the initial classification. An extensive evaluation of the proposed methods is performed, with quantitative comparison against several state-of-the-art approaches on a standard dataset. The experimental results show that the proposed schemes provide equivalent or better results with respect to the competing approaches on most of the considered scenes, both for the task of objects segmentation and for the task of semantic labeling. In particular, the optimal number of segments is automatically provided by the iterative procedures, while it must be arbitrarily set in advance on several other segmentation algorithms. Moreover, no assumption is done on the objects shape, while some competing methods are optimized for planar surfaces. This is provided by the usage of NURBS surfaces as geometric model, since they can represent both simple entities as planes, spheres, cylinders, and complex free-form shapes