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Scene segmentation is a well-known problem in computer vision traditionally tackled by exploiting only the color information from a single scene view. Recent hardware and software developments allow to estimate in real-time scene geometry and open the way for new scene segmentation approaches based on the fusion of both color and depth data. This paper follows this rationale and proposes a novel segmentation scheme where multidimensional vectors are used to jointly represent color and depth data and normalized cuts spectral clustering is applied to them in order to segment the scene. The critical issue of how to balance the two sources of information is solved by an automatic procedure based on an unsupervised metric for the segmentation quality. An extension of the proposed approach based on the exploitation of both images in stereo vision systems is also proposed. Different acquisition setups, like time-of-flight cameras, the Microsoft Kinect device and stereo vision systems have been used for the experimental validation. A comparison of the effectiveness of the different depth imaging systems for segmentation purposes is also presented. Experimental results show how the proposed algorithm outperforms scene segmentation algorithms based on geometry or color data alone and also other approaches that exploit both clues

Abstract-This work introduces an original method for registering pairs of 3D views consisting of range data sets which operates in the frequency domain. The Fourier transform allows the decoupling of the estimate of the rotation parameters from the estimate of the translation parameters, our algorithm exploits this well-known property by suggesting a three-step procedure. The rotation parameters are estimated by the first two steps through convenient representations and projections of the Fourier transforms' magnitudes and the translational displacement is recovered by the third step by means of a standard phase correlation technique after compensating one of the two views for rotation. The performance of the algorithm, which is well-suited for unsupervised registration, is clearly assessed through extensive testing with several objects and shows that good and robust estimates of 3D rigid motion are achievable. Our algorithm can be used as a prealignment tool for more accurate space-domain registration techniques, like the ICP algorithm.

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Abstract. Depth estimation for dynamic scenes is a challenging and relevant problem in computer vision. Although this problem can be tackled by means of ToF cameras or stereo vision systems, each of the two systems alone has its own limitations. In this paper a framework for the fusion of 3D data produced by a ToF camera and a stereo vision system is proposed. Initially, depth data acquired by the ToF camera are up-sampled to the spatial resolution of the stereo vision images by a novel up-sampling algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity field is obtained by a stereo vision algorithm. Finally, the up-sampled ToF depth data and the disparity field provided by stereo vision are synergically fused by enforcing the local consistency of depth data. The depth information obtained with the proposed framework is characterized by the high resolution of the stereo vision system and by an improved accuracy with respect to the one produced by both subsystems. Experimental results clearly show how the proposed method is able to outperform the compared fusion algorithms.