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Most stereo correspondence algorithms match support windows at integer-valued disparities and assume a constant disparity value within the support window. The recently proposed PatchMatch stereo algorithm [7] overcomes this limitation of previous algorithms by directly estimating planes. This work presents a method that integrates the PatchMatch stereo(More)
— In this paper we give an evaluation of different methods for computing frame-to-frame motion estimates for a moving RGB-D sensor, by means of aligning two images using photometric error minimization. These kind of algorithms have recently shown to be very accurate and robust and therefore provide an attractive solution for robot ego-motion estimation and(More)
— In this paper, we present a system for controlling a quadrocopter using both optical and inertial measurements. We show how to use external stereo camera measurements for visual servoing, by onboard fusion at high rates, only natural features provided by the vehicle and without any active marker. In our experiments, we show the accuracy and robustness of(More)
We propose a novel method for planar hand detection from a single uncalibrated image, with the purpose of estimating the articulated pose of a generic model, roughly adapted to the current hand shape. The proposed method combines line and point correspondences, associated to finger tips, lines and concavities, extracted from color and intensity edges. The(More)
In this work we propose a novel approach to the problem of multi-view stereo reconstruction. Building upon the previously proposed PatchMatch stereo and PM-Huber algorithm we introduce an extension to the multi-view scenario that employs an iterative refinement scheme. Our proposed approach uses an extended and robustified volumetric truncated signed(More)
In this paper, we propose a general-purpose methodology for detecting multiple objects with known visual models from multiple views. The proposed method is based Monte-Carlo sampling and weighted mean-shift clustering, and can make use of any model-based likelihood (color, edges, etc.), with an arbitrary camera setup. In particular , we propose an algorithm(More)
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