Showing papers by "Emanuele Rodolà published in 2010"

Robust Camera Calibration using Inaccurate Targets

Abstract: Accurate intrinsic camera calibration is essential to any computer vision task that involves image based measurements. Given its crucial role with respect to precision, a large number of approaches have been proposed over the last decades. Despite this rich literature, steady advancements in imaging hardware regularly push forward the need for even more accurate techniques. Some authors suggest generalizations of the camera model itself, others propose novel designs for calibration targets or different optimization schemas. In this paper we take a completely different route by directly addressing one of the most overlooked problems in practical calibration scenarios. Specifically, we drop the assumption that the target is known with enough precision and we adjust it in an iterative way as part of the whole process. This is in fact the case with the typical target used in most of the calibration literature, which is usually printed on paper and stitched on a flat surface. In the experimental section we show that even with such a cheaply crafted target it is possible to obtain a very accurate camera calibration that outperforms those obtained with well-known standard techniques.

A game-theoretic approach to fine surface registration without initial motion estimation

Abstract: Surface registration is a fundamental step in the reconstruction of three-dimensional objects. This is typically a two step process where an initial coarse motion estimation is followed by a refinement. Most coarse registration algorithms exploit some local point descriptor that is intrinsic to the shape and does not depend on the relative position of the surfaces. By contrast, refinement techniques iteratively minimize a distance function measured between pairs of selected neighboring points and are thus strongly dependent on initial alignment. In this paper we propose a novel technique that allows to obtain a fine surface registration in a single step, without the need of an initial motion estimation. The main idea of our approach is to cast the selection of correspondences between points on the surfaces in a game-theoretic framework, where a natural selection process allows mating points that satisfy a mutual rigidity constraint to thrive, eliminating all the other correspondences. This process yields a very robust inlier selection scheme that does not depend on any particular technique for selecting the initial strategies as it relies only on the global geometric compatibility between correspondences. The practical effectiveness of the proposed approach is confirmed by an extensive set of experiments and comparisons with state-of-the-art techniques.

Loosely distinctive features for robust surface alignment

Abstract: Many successful feature detectors and descriptors exist for 2D intensity images. However, obtaining the same effectiveness in the domain of 3D objects has proven to be a more elusive goal. In fact, the smoothness often found in surfaces and the lack of texture information on the range images produced by conventional 3D scanners hinder both the localization of interesting points and the distinctiveness of their characterization in terms of descriptors. To overcome these limitations several approaches have been suggested, ranging from the simple enlargement of the area over which the descriptors are computed to the reliance on external texture information. In this paper we offer a change in perspective, where a game-theoretic matching technique that exploits global geometric consistency allows to obtain an extremely robust surface registration even when coupled with simple surface features exhibiting very low distinctiveness. In order to assess the performance of the whole approach we compare it with state-of-the-art alignment pipelines. Furthermore, we show that using the novel feature points with well-known alternative non-global matching techniques leads to poorer results.

Robust Game-Theoretic Inlier Selection for Bundle Adjustment

Abstract: Bundle Adjustment is a widely adopted self-calibration technique that allows to estimate scene structure and camera parameters at once. Typically this happens by iteratively minimizing the reprojection error between a set of 2D stereo correspondences and their predicted 3D positions. This optimization is almost invariantly carried out by means of the Levenberg-Marquardt algorithm, which is very sensitive to the presence of outliers in the input data. For this reason many structure-from-motion techniques adopt some inlier selection algorithm. This usually happens both in the initial feature matching step and by pruning matches with larger reprojection error after an initial optimization. While this works well in many scenarios, outliers that are not filtered before the optimization can still lead to wrong parameter estimation or even prevent convergence. In this paper we introduce a novel stereo correspondences selection schema that exploits Game Theory in order to perform a robust inlier selection before any optimization step. The practical effectiveness of the proposed approach is confirmed by an extensive set of experiments and comparisons with stateof-the-art techniques.

Robust Figure Extraction on Textured Background: A Game-Theoretic Approach

Abstract: Feature-based image matching relies on the assumption that the features contained in the model are distinctive enough. When both model and data present a sizeable amount of clutter, the signal-to-noise ratio falls and the detection becomes more challenging. If such clutter exhibits a coherent structure, as it is the case for textured background, matching becomes even harder. In fact, the large amount of repeatable features extracted from the texture dims the strength of the relatively few interesting points of the object itself. In this paper we introduce a game-theoretic approach that allows to distinguish foreground features from background ones. In addition the same technique can be used to deal with the object matching itself. The whole procedure is validated by applying it to a practical scenario and by comparing it with a standard point-pattern matching technique.

A Game-Theoretic Approach to Robust Selection of Multi-view Point Correspondence

Abstract: In this paper we introduce a robust matching technique that allows very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of subsequent verification through reprojection. The global consistency is reduced to a pairwise compatibility making use of the size and orientation information provided by common feature descriptors, thus projecting what is a high-order compatibility problem into a pairwise setting. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction.

A game-theoretic approach to the enforcement of global consistency in multi-view feature matching

Abstract: In this paper we introduce a robust matching technique that allows to operate a very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of ex-post verification through reprojection. Two forms of global consistency are proposed, but in both cases they are reduced to pairwise compatibilities making use of the size and orientation information provided by common feature descriptors. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction. The effectiveness of the approach in estimating camera parameters for bundle adjustment is assessed and compared with state-of-the-art techniques.