A Geometric and Hypothesis-Based Method for Low-Overlap, Sparse, and Featureless Point Set Matching

Three‐dimensional (3D) point cloud registration is a fundamental key issue in 3D reconstruction, 3D object recognition and augmented reality. In this study, the authors propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, they propose a 3D point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. They can use these matching sets to calculate multiple transformations between two point clouds. Finally, they use the error function to identify the best transformation and to achieve coarse alignment of the two point clouds. Experiments and comparisons with other global algorithms demonstrate that the proposed approach can be applied to register point clouds with considerable or limited overlaps and is robust to noise.

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

We focus on a surface evolution problem where the local surface speed depends on a computationally expensive scalar function with non-local properties. The local surface speed must be re-evaluated in each time step, even for non-moving parts of the surface, due to possibly changed properties in remote regions of the simulation domain. We present a method to evaluate the surface speed only on a sparse set of points to reduce the computational effort. This sparse set of points is generated according to application-specific requirements using an iterative partitioning scheme. We diffuse the result of a constant extrapolation in the neighborhood of the sparse points to obtain an approximation to a linear interpolation between the sparse points.

We address the problem of perceptual grouping from motion cues by formulating it as a motion layers inference from a sparse and noisy point set in a 4D space. Our approach is based on a layered 4D representation of data, and a voting scheme for token communication, within a tensor voting computational framework. Given two sparse sets of point tokens, the image position and potential velocity of each token are encoded into a 4D tensor. By enforcing the smoothness of motion through a voting process, the correct velocity is selected for each input point as the most salient token. An additional dense voting step allows for the inference of a dense representation in terms of pixel velocities, motion regions, and boundaries. Using a 4D space for this tensor voting approach is essential since it allows for a spatial separation of the points according to both their velocities and image coordinates. Unlike most other methods that optimize certain objective functions, our approach is noniterative and, therefore, does not suffer from local optima or poor convergence problems. We demonstrate our method with synthetic and real images, by analyzing several difficult cases-opaque and transparent motion, rigid and nonrigid motion, curves and surfaces in motion.

This paper casts surface registration as the problem of finding a set of discrete correspondences through the minimization of an energy function, which is composed of geometric and appearance matching costs, as well as higher-order deformation priors. Two higher-order graph-based formulations are proposed under different deformation assumptions. The first formulation encodes isometric deformations using conformal geometry in a higher-order graph matching problem, which is solved through dual-decomposition and is able to handle partial matching. Despite the isometry assumption, this approach is able to robustly match sparse feature point sets on surfaces undergoing highly anisometric deformations. Nevertheless, its performance degrades significantly when addressing anisometric registration for a set of densely sampled points. This issue is rigorously addressed subsequently through a novel deformation model that is able to handle arbitrary diffeomorphisms between two surfaces. Such a deformation model is introduced into a higher-order Markov Random Field for dense surface registration, and is inferred using a new parallel and memory efficient algorithm. To deal with the prohibitive search space, we also design an efficient way to select a number of matching candidates for each point of the source surface based on the matching results of a sparse set of points. A series of experiments demonstrate the accuracy and the efficiency of the proposed framework, notably in challenging cases of large and/or anisometric deformations, or surfaces that are partially occluded.

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm.

Rapid advancement of 3D sensing techniques has led to dense and accurate point cloud of an object to be readily available. The growing use of such scanned point sets in product design, analysis, and manufacturing necessitates research on direct processing of point set surfaces. In this paper, we present an approach that enables the direct layered manufacturing of point set surfaces. This new approach is based on adaptive slicing of moving least squares (MLS) surfaces. Salient features of this new approach include the following: (1) It bypasses the laborious surface reconstruction and avoids model conversion induced accuracy loss. (2) The resulting layer thickness and layer contours are adaptive to local curvatures, and thus it leads to better surface quality and more efficient fabrication. (3) The curvatures are computed from a set of closed formula based on the MLS surface. The MLS surface naturally smoothes the point cloud and allows upsampling and downsampling, and thus it is robust even for noisy or sparse point sets. Experimental results on both synthetic and scanned point sets are presented.

ABSTRACTReliable feature point matching is a pivotal step in the integrated 3D reconstruction of aerial and ground images. Nonetheless, the substantial viewpoint and scale differences between aerial and ground images introduce significant challenges in feature point matching. To address this issue, this study presents a robust matching method for feature points from these two types of images, employing partitioned attention aggregation along with a coarse‐to‐fine hierarchical matching approach, enabling efficient matching of large‐scale aerial‐ground images even with hardware limitations. In the coarse matching phase, region segmentation is carried out on the ground image based on its depth map, which is computed using a monocular depth estimation network. Subsequently, a graph neural network serves as an intermediary for information exchange between features, and partitioned attention aggregation is performed while adhering to the constraints imposed by the segmented image regions. During this process, feature points are grouped to form distinct subgraphs, enhancing the structural similarity between corresponding graphs when there are significant depth and viewpoint changes. This also reduces redundant connections within graph neural networks, leading to efficient feature matching. Corresponding local patches are generated from the full‐sized aerial‐ground images using the initial matches, and further feature point extraction and matching from these patches yield accurate point matches. The experimental results indicate the proposed method performs better than state‐of‐the‐art matching methods. Specifically, compared with the best‐performing method among the comparative ones, our method enhances the average number of correct matches and the average matching precision by approximately 6‐fold and 36%, respectively.

Education plays a fundamental role in the personal and social development of individuals. However, in some cases, educational processes occur under challenging conditions, causing various difficulties for students and educators. Challenging conditions can result from natural disasters, wars, economic crises, or health problems. This research aimed to describe education, literacy, and leadership in challenging conditions through the opinions of teachers and schools. In this context, it sought answers to the questions of what it is, how it is, and how it should be. It aimed to analyze and evaluate schools from the perspective of principals and teachers in an emergency and difficult situation after the earthquake in Hatay, Türkiye. The research examined the challenges school leaders face in doing their jobs in an emergency or challenging circumstances, the nature of the context in which these challenges occur, and the strategies they adopt to meet these challenges. The context of the research was a situation study of urgent and challenging conditions in Hatay province, based on the opinions of school principals (in primary and high schools) working under challenging conditions in Hatay. The situation in question in this study is the management and leadership actions of education in emergency and difficult conditions caused by the earthquake in Hatay. This study included primary and high schools located in Hatay, facing challenging conditions. A descriptive analysis was performed to analyze the qualitative data. Leadership in emergency and difficult situations is an important factor in raising awareness for teachers, students, and parents to determine a common direction and combat difficulties in this process because challenging environments have a negative impact on teachers and students. It is possible to say that the most basic support element of a school’s different stakeholders in this difficult situation is school principals. It was concluded that appropriate and qualified coordination and the leadership of school principals in difficult and emergency situations are important factors. Competence in education and leadership in difficult situations will increase literacy in this area.

In this paper, a modal expansion method is proposed to compute the field distribution inside a reverberation chamber excited by a monopole. The electromagnetic fields are expanded using modal functions derived from Helmholtz equation. The continuity conditions of tangential field components at regional interfaces are transformed into four matrix equations through the mode matching (MM) process. Another two matrix equations are obtained using the point matching (PM) method by imposing the boundary conditions on the four side walls. With those six matrix equations, all the unknown expansion coefficients are determined. This technique takes advantage of the orthogonality of the modal functions, and thus the obtained matrix is block-diagonal, which is easy to be inverted. Furthermore, discretization of the whole chamber is avoided so that the problem scale is greatly reduced. Therefore, the proposed method appears to provide a rigorous and efficient analysis of RC excited by a monopole antenna.

A night pavement crack detection method based on image‐to‐image translation

We study the problems of upsampling a low-resolution depth map and interpolating an initial set of sparse motion matches, with the guidance from a corresponding high-resolution color image. The common objective for both tasks is to densify a set of sparse data points, either regularly distributed or scattered, to a full image grid through a 2D guided interpolation process. We propose a unified approach that casts the fundamental guided interpolation problem into a hierarchical, global optimization framework. Built on a weighted least squares (WLS) formulation with its recent fast solver – fast global smoothing (FGS) technique, our method progressively densifies the input data set by efficiently performing the cascaded, global interpolation (or smoothing) with alternating guidances. Our cascaded scheme effectively addresses the potential structure inconsistency between the sparse input data and the guidance image, while preserving depth or motion boundaries. To prevent new data points of low confidence from contaminating the next interpolation process, we also prudently evaluate the consensus of the interpolated intermediate data. Experiments show that our general interpolation approach successfully tackles several notorious challenges. Our method achieves quantitatively competitive results on various benchmark evaluations, while running much faster than other competing methods designed specifically for either depth upsampling or motion interpolation.

The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction. The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based initialization. Depending on the surface-based matching techniques, the reconstruction errors were slightly different. When a surface-based iterative affine registration was used, an average reconstruction error of 1.6 mm was observed. This error was increased to 1.9 mm, when a surface-based iterative scaled rigid registration was used. It is feasible to reconstruct a scaled, patient-specific surface model of the pelvis from single standard AP x-ray radiograph using the present approach. The unknown scale of the reconstructed model can be estimated by performing a surface-based 3D/3D matching.

Accurate contaminant prediction is very important for risk assessment and site remediation. Several predictive tools in the form of mathematical models have been used to model the movement and behavior of contaminants in groundwater. These deterministic models do not account for the heterogeneity and uncertainties in the subsurface environment. Discretization of the models spatially and temporally introduces approximation and truncation errors. In this research, to improve the accuracy of subsurface contaminant prediction in time and space and to assess the impact of first-order decay rate parameter estimation, Kalman filter coupled with Monte Carlo sampling was used in a specified three-dimensional domain space. The filter is perturbed with random Gaussian noise to reflect real life case of contaminant movement. Set of sparse observation points selected at specific locations are used to guide the filter at every time step to improve the accuracy of the prediction. The first-order decay rate parameter is estimated using Monte Carlo sampling method. The algorithms to generate the simulation results were run on Matlab 7.1. The efficacy of the Kalman filter coupled with Monte Carlo sampling, Kalman filter without Parameter Estimation and the numerical method were tested using Mean Absolute Error (MAE) and Maximum Absolute Error (Emax) equations. The results show that the Kalman filter coupled with Monte Carlo sampling performs better than both the Kalman filter without Parameter Estimation and the numerical method. Also, the Kalman filter coupled with Monte Carlo sampling is capable of reducing the error in the numerical solution by approximately 75% when Mean Absolute Error equation is used to estimate the prediction error. The error reduction is due to the adaptive nature of the Kalman filter to the observation data used in the simulation.

Efficient algorithms exist to obtain a sparse 3D representation of the environment. Bundle adjustment (BA) and structure from motion (SFM) are techniques used to estimate both the camera poses and the set of sparse points in the environment. Many applications require such reconstruction to be performed online, while acquiring the data, and produce an updated result every step. Furthermore, using active feedback about the quality of the reconstruction can help selecting the best views to increase the accuracy as well as to maintain a reasonable size of the collected data. This paper provides novel and efficient solutions to solving the associated NLS incrementally, and to compute not only the optimal solution, but also the associated uncertainty. The proposed technique highly increases the efficiency of the incremental BA solver for long camera trajectory applications, and provides extremely fast covariance recovery.