KEYWORDS: Point clouds, 3D modeling, Clouds, Neural networks, Databases, Education and training, Matrices, Evolutionary algorithms, Network architectures, Singular value decomposition
Recently, there has been essential progress in the field of deep learning, which has led to compelling advances in most of the semantic tasks of computer vision, such as classification, detection, and segmentation. Point cloud registration is a task that aligns two or more different point clouds by evaluating the relative transformation between them. The Iterative Closest Points (ICP) algorithm and its variants have relatively good computational efficiency but are known to be subject to local minima, so rely on the quality of the initialization. In this paper, we propose a neural network based on the Deep Closest Points (DCP) neural network to solve the point cloud registration problem for incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.
The important task of 2D image classification and segmentation is the extraction of the local geometrical features. The convolution neural network is the common approach last years in this field. Usually, the neighborhood of each pixel of the image is implemented to collect local geometrical information. The information for each pixel is stored in a matrix. Then, Convolutional Auto-Encoder (CAE) is utilized to extract the main geometrical features. In this paper, we propose a neural network based on CAE to solve the extraction of local geometrical features problem for noisy images. Computer simulation results are provided to illustrate the performance of the proposed method.
Point cloud is an important type of geometric data structure. Various applications require high-level point cloud processing. Instead of defining geometric elements such as corners and edges, state-of-the-art algorithms use semantic matching. These methods require learning-based approaches that rely on a statistical analysis of labeled datasets. Adapting deep learning techniques to handle 3D point clouds remains challenging. The standard deep neural network model requires regular inputs such as vectors and matrices. Three-dimensional point clouds are fundamentally irregular; that is, the positions of points are continuously distributed in space, and any permutation of their order does not change the spatial distribution. Modern deep neural networks are designed specifically to process point clouds directly, without going to an intermediate regular representation. The Deep Closest Point (DCP) network is a neural network that implements the ICP algorithm. DCP utilizes the point-to-point functional for error metric minimization. In this paper, we propose the modified variant of DCP based on other types of ICP error minimization functionals. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Geometric registration is a key task in many computational fields, including medical imaging, robotics, autonomous driving. The registration involves the prediction of a rigid motion to align one point cloud to another, potentially distorted by noise and partiality. The most popular point cloud registration algorithm, Iterative Closest Point (ICP), alternates between estimating the rigid motion based on a fixed correspondence estimate and updating the correspondences to their closest matches. Recently, the success of deep neural networks for image processing has motivated an approach to learning features on point clouds. Adaptation of deep learning to analyze point cloud data is far from straightforward. Most critically, standard deep neural network models require input data with regular structure, while point clouds are fundamentally irregular: Point positions are continuously distributed in the space, and any permutation of their ordering does not change the spatial distribution. Several neural networks have recently been proposed for analyzing point clouds data such as PointNet and DGCNN. In this paper, we propose a permutation invariant neural network to identify matching pairs of points in the clouds. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Human facial expressions describe a set of signals, which can be associated with mental states such as emotions depending on physiological conditions. There are many potential applications of expression recognition systems. They take into account about two hundred emotional states. Expression recognition is a challenging problem, not only due to the variety of expressions, but also due to difficulty in extraction of effective features from facial images. Depending on a 3D reconstruction technique, 3D data can be immune to a great range of illumination and texture variations, and they are no sensitive as 2D images to out-of-plane rotations. Moreover, 2D images may fail to capture subtle but discriminative change on the face if there is no sufficient change in brightness, such as bulges on the cheeks and protrusion of the lips. In fact, 3D data yield better recognition than conventional 2D data for many types of facial actions The most effective tool for solution of the problem of human face recognition is neural networks. But the result of recognition can be spoiled by facial expressions and other deviation from canonical face representation. In the proposed presentation we describe a resampling method of human faces represented by 3D point clouds. The method based on the non-rigid ICP (Iterative Closest Point) algorithm. We consider the combined using of this method and convolutional neural network (CNN) in the face recognition task. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
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