Reconstruction of 3D map of the observed scene in real space is based on the information on 3D points coordinates. Triangulation is the known method for surface representation in three-dimensional space. Two consistently obtained frames with point clouds corresponds to two partially overlapping triangulated surfaces. There is known an algorithm for the correct construction of the triangulation of the overlapping area. Another approach to the build of a 3D map is the use of surfels. Surfel is a round patch on the surface. The characteristics of the surfels are described by a triple of elements: the position of the surfel, the normal to the surfel, the radius of the surfel. One more recently developed method for constructing a three-dimensional map of the scene is the so-called “octree”. Octrees are a hierarchical data structure for a spatial representation of an object. Each octree node represents a space contained in a cubic volume called a voxel. This volume is recursively subdivided into eight sub-volumes until the specified minimum voxel size is reached. The minimum voxel size determines the resolution of the octree. Since octree is a hierarchical data structure, a tree can be reduced at any level to get a coarser spatial representation of the object. The decision on whether a given voxel is a busy object or not is made on the basis of a probabilistic approach. In the proposed paper we describe new efficient algorithm for surface reconstruction in three-dimensional space and with the help of computer simulation, the proposed method is compared with known algorithms for 3D map reconstruction.
ICP is the most commonly used algorithm in tasks of point clouds mapping, finding the transformation between clouds, building a three-dimensional map. One of the key steps of the algorithm is the removal a part of the points and the searching a correspondence of clouds. In this article, we propose a method for removing some points from the clouds. Reducing the number of points decrease an execution time of the next steps and, as a result, increase performance. The paper describes an approach based on the analysis of the geometric shapes of the scene objects. In the developed algorithm, the points lying on the boundaries of the planes intersections, the so-called edges of objects, are selected from the clouds. Then the intersection points of the found edges are checked to belong the main vertices of the objects. After that, additional vertices are excluded from the edges and, if necessary, new ones are added. The described approach is performed for both point clouds. All further steps of the ICP algorithm are performed with new clouds. In the next step, after finding the correspondence, the vertices found in the previous step are taken from the first cloud, with all the edges connected with them. For each such group it is necessary to find the corresponding group from the second cloud. The method looks for correspondence for geometrically similar parts of point clouds. After finding the intermediate transformation, the current error is calculated. The original point clouds are used for the error calculation. This approach significantly reduces the number of points participating the deciding of the ICP variational subproblem.
The problem of aligning of 3D point data is the known registration task. The most popular registration algorithm is the Iterative Closest Point (ICP) algorithm. The traditional ICP algorithm is a fast and accurate approach for rigid registration between two point clouds but it is unable to handle affine case. Recently, extension of the ICP algorithm for composition of scaling, rotation, and translation is proposed. A generalized ICP version for an arbitrary affine transformation is also suggested. In this paper, a new iterative algorithm for registration of point clouds based on the point-to-plane ICP algorithm with affine transformations is proposed. At each iteration, a closed-form solution to the affine transformation is derived. This approach allows us to get a precise solution for transformations such as rotation, translation, and scaling. With the help of computer simulation, the proposed algorithm is compared with common registration algorithms.
KEYWORDS: Direct methods, Image registration, Visual process modeling, 3D image processing, Optical tracking, Detection and tracking algorithms, Computer simulations
Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.
The problem of aligning of 3D point data is the known registration task. The most popular registration algorithm is the Iterative Closest Point (ICP) algorithm. The traditional ICP algorithm is a fast and accurate approach for rigid registration between two point clouds but it is unable to handle affine case. Recently, extension of the ICP algorithm for composition of scaling, rotation, and translation is proposed. A generalized ICP version for an arbitrary affine transformation is also suggested. In this paper, a new iterative algorithm for registration of point clouds based on the point-to-plane ICP algorithm with affine transformations is proposed. At each iteration, a closed-form solution to the affine transformation is derived. This approach allows us to get a precise solution for transformations such as rotation, translation, and scaling. With the help of computer simulation, the proposed algorithm is compared with common registration algorithms.
The iterative closest point (ICP) algorithm is one of the most popular approaches to shape registration. The algorithm
starts with two point clouds and an initial guess for a relative rigid-body transformation between them. Then it iteratively
refines the transformation by generating pairs of corresponding points in the clouds and by minimizing a chosen error
metric. In this work, we focus on accuracy of the ICP algorithm. An important stage of the ICP algorithm is the
searching of nearest neighbors. We propose to utilize for this purpose geometrically similar groups of points. Groups of
points of the first cloud, that have no similar groups in the second cloud, are not considered in further error minimization.
To minimize errors, the class of affine transformations is used. The transformations are not rigid in contrast to the
classical approach. This approach allows us to get a precise solution for transformations such as rotation, translation
vector and scaling. With the help of computer simulation, the proposed method is compared with common nearest
neighbor search algorithms for shape registration.
In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed
face recognition method contains several components. In particular this is rigid body image registration. The rigid
registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine
transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is
inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for
the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring
algorithms to segment the subject’s face; thermal matching based on partial least squares discriminant analysis. Thermal
imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised
faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near
infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in
real indoor environments is presented and discussed.
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