Results 11  20
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3,113
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
, 1994
"... A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot use ..."
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Cited by 278 (9 self)
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A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses twodimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacementbetween the scans. The second algorithm establishes correspondences between points in the two scans and then solves the pointtopoint leastsquares probl...
A spectral technique for correspondence problems using pairwise constraints
 In International Conference on Computer Vision
, 2005
"... Abstract We present an efficient spectral method for finding consistent correspondences between two sets of features. We build the adjacency matrix M of a graph whose nodes represent the potential correspondences and the weights on the links represent pairwise agreements between potential correspon ..."
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Cited by 251 (10 self)
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Abstract We present an efficient spectral method for finding consistent correspondences between two sets of features. We build the adjacency matrix M of a graph whose nodes represent the potential correspondences and the weights on the links represent pairwise agreements between potential correspondences. Correct assignments are likely to establish links among each other and thus form a strongly connected cluster. Incorrect correspondences establish links with the other correspondences only accidentally, so they are unlikely to belong to strongly connected clusters. We recover the correct assignments based on how strongly they belong to the main cluster of M , by using the principal eigenvector of M and imposing the mapping constraints required by the overall correspondence mapping (onetoone or onetomany). The experimental evaluation shows that our method is robust to outliers, accurate in terms of matching rate, while being much faster than existing methods.
A Pyramid Approach to SubPixel Registration Based on Intensity
, 1998
"... We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (2D) or volumes (3D). It uses an explicit spline representation of the images in conjunction with spline processing, and ..."
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Cited by 237 (18 self)
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We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (2D) or volumes (3D). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarsetofine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the MarquardtLevenberg algorithm for nonlinear leastsquare optimization. The geometric deformation model is a global 3D affine transformation that can be optionally restricted to rigidbody motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable singlestage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the MarquardtLevenberg algorithm is faster.
Multiview Registration for Large Data Sets
, 1999
"... In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitabl ..."
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Cited by 222 (1 self)
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In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections.
Computing and rendering point set surfaces.
 IEEE Transactions on Visualization and Computer Graphics,
, 2003
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A survey of freeform object representation and recognition techniques
 Computer Vision and Image Understanding
, 2001
"... Advances in computer speed, memory capacity, and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) threedimensional models feasible. These models are either painstakingly designed through an elaborate CAD process or re ..."
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Cited by 200 (1 self)
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Advances in computer speed, memory capacity, and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) threedimensional models feasible. These models are either painstakingly designed through an elaborate CAD process or reverse engineered from sculpted prototypes using modern scanning technologies and integration methods. The availability of detailed data describing the shape of an object offers the computer vision practitioner new ways to recognize and localize freeform objects. This survey reviews recent literature on both the 3D model building process and techniques used to match and identify freeform objects from imagery. c ○ 2001 Academic Press 1.
A levelset approach to 3d reconstruction from range data
 International Journal of Computer Vision
, 1998
"... This paper presents a method that uses the level sets of volumes to reconstruct the shapes of 3D objects from range data. The strategy is to formulate 3D reconstruction as a statistical problem: find that surface which is mostly likely, given the data and some prior knowledge about the application d ..."
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Cited by 195 (24 self)
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This paper presents a method that uses the level sets of volumes to reconstruct the shapes of 3D objects from range data. The strategy is to formulate 3D reconstruction as a statistical problem: find that surface which is mostly likely, given the data and some prior knowledge about the application domain. The resulting optimization problem is solved by an incremental process of deformation. We represent a deformable surface as the level set of a discretely sampled scalar function of 3 dimensions, i.e. a volume. Such levelset models have been shown to mimic conventional deformable surface models by encoding surface movements as changes in the greyscale values of the volume. The result is a voxelbased modeling technology that offers several advantages over conventional parametric models, including flexible topology, no need for reparameterization, concise descriptions of differential structure, and a natural scale space for hierarchical representations. This paper builds on previous work in both 3D reconstruction and levelset modeling. It presents a fundamental result in surface estimation from range data: an analytical characterization of the surface that maximizes the posterior probability. It also presents a novel computational technique for levelset modeling, called the sparsefield algorithm, which combines the advantages of a levelset approach with the computational efficiency and accuracy of a parametric representation. The sparsefield algorithm is more efficient than other approaches, and because it assigns the level set to a specific set of grid points, it positions the levelset model more accurately than the grid itself. These properties, computational efficiency and subcell accuracy, are essential when trying to reconstruct the shapes of 3D objects. Results are shown for the reconstruction objects from sets of noisy and overlapping range maps.
A New Algorithm for NonRigid Point Matching
 IN CVPR
, 2000
"... We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and nonrigid transformations between two pointsets that may be of different sizes. The algorithm utilizes the softassign for the correspondence and the thinplate spline for the nonrigid mapping. E ..."
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Cited by 191 (8 self)
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We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and nonrigid transformations between two pointsets that may be of different sizes. The algorithm utilizes the softassign for the correspondence and the thinplate spline for the nonrigid mapping. Embedded within a deterministic annealing framework, the algorithm can automatically reject a fraction of the points as outliers. Experiments on both 2D synthetic pointsets with varying degrees of deformation, noise and outliers, and on real 3D sulcal pointsets (extracted from brain MRI) demonstrate the robustness of the algorithm.
Meshless deformations based on shape matching
 ACM TRANS. GRAPH
, 2005
"... We present a new approach for simulating deformable objects. The underlying model is geometrically motivated. It handles pointbased objects and does not need connectivity information. The approach does not require any preprocessing, is simple to compute, and provides unconditionally stable dynamic ..."
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Cited by 169 (12 self)
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We present a new approach for simulating deformable objects. The underlying model is geometrically motivated. It handles pointbased objects and does not need connectivity information. The approach does not require any preprocessing, is simple to compute, and provides unconditionally stable dynamic simulations. The main idea of our deformable model is to replace energies by geometric constraints and forces by distances of current positions to goal positions. These goal positions are determined via a generalized shape matching of an undeformed rest state with the current deformed state of the point cloud. Since points are always drawn towards welldefined locations, the overshooting problem of explicit integration schemes is eliminated. The versatility of the approach in terms of object representations that can be handled, the efficiency in terms of memory and computational complexity, and the unconditional stability of the dynamic simulation make the approach particularly interesting for games.