Range Image Registration Research Articles

Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates

Abstract Computer-aided medical systems, e.g. in the fields of medical robotics or image-based assistance, are continuously investigated to overcome human limitations concerning perception, memory or dexterity. A common requirement of such systems is the availability of a digital model describing the patient’s position and morphology during a procedure. Operational complexity and technical limitations of established 3D imaging methods leave clinical settings in need of a method for the fast acquisition of a three-dimensional body surface representation. For this purpose, we propose an unsupervised and efficient body registration pipeline based on the markerless elastic registration and completion of single-view stereo range images of the body surface with statistical parametric body shape templates. Initial results show a promising representative quality of the models generated through the registration process with submillimetric fitting accuracy and realistic surface morphology, indicating the general feasibility of our approach as an instant body registration method for automated medical and biometric applications.

Novel invariant feature descriptor and a pipeline for range image registration in robotic welding applications

This work proposes an invariant descriptor and a pipeline for the registration of surface range images based on segmentation/reconstruction making use of an edge detection technique combined with a clustering technique using mesh decimation. This novel descriptor is applied to contours and it is invariant to similarity transformations including rotation, translation, uniform scale and it is robust to noise. The proposed feature descriptor makes use of corresponding points extracted from two images and a signature label is assigned specifically to a point considering the geometrical distribution of its neighbourhood, reducing possible areas of overlapping and the ambiguity in the search process. The descriptor was evaluated through a series of tests with various object range images. To validate the candidate transformations, the fitting errors between the two range images are evaluated by the iterative closest point algorithm. This study also presents and discusses results from the application of the developed pipeline in a vision sensor mounted on a robot arm specially built as part of a R&D project to acquire range images by laser scanning over the surface of hydraulic turbine blades. The sensor generates 3D surface models to be registered in the 3D coordinate system of the robot controller.

Free-Form Surface Analysis and Linking Strategies for High Registration Accuracy in Quality Assurance Applications

Robot-based optical inspection systems have become more affordable and show great potential for measuring geometric features of sheet metal parts in production sites. To deal with the lack of positioning accuracy of current industrial robots for the registration of range images, a stitching algorithm is often applied. This paper presents a concept to improve the fine registration accuracy in order to reduce the effect of error propagation. A geometric analysis of the surface is introduced to identify suitable regions for applying adapted texture projections onto the part. In addition, an approach to develop a corresponding linking strategy is described. The proposed concept promises to measure geometric features on both plane and highly featured surfaces with high accuracy.

Exploiting multi-core and GPU hardware to speed up the registration of range images by means of Differential Evolution

Within this paper a general-purpose distributed evolutionary algorithm is presented, and is applied to the pair-wise registration of range images. Registration is carried out by utilizing the Grid Closest Point (GCP) for the graphical registration operations and the distributed algorithm to search for the best possible transformation of a scene image that, merged with the model image, yields a 3D reconstruction of the original object. The evolutionary algorithm is a distributed Differential Evolution algorithm that exploits an asynchronous migration mechanism and a multi-population recombination information exchange. Such an algorithm is provided with an adaptive updating scheme based on chaotic features for dynamically updating the control parameters.The scope of the paper is to speed up the registration process by using processor specialized to handle graphical operations and multi-core platforms. On the one hand, we investigate the use of either Graphic Processing Units (GPUs) or multi-core architectures to lower the execution time of the GCP procedure. On the other hand, we evaluate the performance of the distributed evolutionary algorithm in terms of solution quality by examining different multi-core architectures.Experimental results on a set of publicly available images show that, to perform the GCP, reductions in the execution times by one order of magnitude are obtained by harnessing the computational power of GPU and multi-core platforms with respect to the execution on a CPU-based framework. Furthermore, a comparison with the state-of-the-art sequential evolutionary algorithm for range image registration reveals that the adaptive distributed Differential Evolution algorithm allows attaining integral 3D models from 3D scan datasets that are better in terms of both quality and robustness.

Contributions of monocular and binocular cues to distance discrimination in natural scenes.

Little is known about distance discrimination in real scenes, especially at long distances. This is not surprising given the logistical difficulties of making such measurements. To circumvent these difficulties, we collected 81 stereo images of outdoor scenes, together with precisely registered range images that provided the ground-truth distance at each pixel location. We then presented the stereo images in the correct viewing geometry and measured the ability of human subjects to discriminate the distance between locations in the scene, as a function of absolute distance (3 m to 30 m) and the angular spacing between the locations being compared (2°, 5°, and 10°). Measurements were made for binocular and monocular viewing. Thresholds for binocular viewing were quite small at all distances (Weber fractions less than 1% at 2° spacing and less than 4% at 10° spacing). Thresholds for monocular viewing were higher than those for binocular viewing out to distances of 15–20 m, beyond which they were the same. Using standard cue-combination analysis, we also estimated what the thresholds would be based on binocular-stereo cues alone. With two exceptions, we show that the entire pattern of results is consistent with what one would expect from classical studies of binocular disparity thresholds and separation/size discrimination thresholds measured with simple laboratory stimuli. The first exception is some deviation from the expected pattern at close distances (especially for monocular viewing). The second exception is that thresholds in natural scenes are lower, presumably because of the rich figural cues contained in natural images.

Range image registration based on hash map and moth-flame optimization

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.

Range image registration based on 2D synthetic images

Proposed in this paper is a new procedure of range image (point cloud) registration reducing the risk of the original Iterative Closest Point (ICP) algorithm which gets stuck in a local minimum. To reduce this risk, it is essential to achieve the fine initial alignment of the given range images. The proposed procedure employs a 2D image registration method to achieve the fine initial alignment instead of a 3D feature based registration method. Although the 2D image registration method has also the risk of mismatching, we minimize the risk by increasing the number of test cases and selecting the answer that gains the most votes. To generate effective test cases, multiple 2D images are synthetized from the given range images by varying the orientation and resolution. The proposed procedure is implemented and tested for various examples, with experimental results showing that it outperforms existing methods.

A performance evaluation method to compare the multi-view point cloud data registration based on ICP algorithm and reference marker

Registration of range images of surfaces is a fundamental problem in three-dimensional modelling. This process is performed by finding a rotation matrix and translation vector between two sets of data points requiring registration. Many techniques have been developed to solve the registration problem. Therefore, it is important to understand the accuracy of various registration techniques when we decide which technique will be selected to perform registration task. This paper presents a new approach to test and compare registration techniques in terms of accuracy. Among various registration methods, iterative closest point-based algorithms and reference marker methods are two types of commonly applied methods which are used to accomplish this task because they are easy to implement and relatively low cost. These two methods have been selected to perform a comprehensively quantitative evaluation by using the proposed method and the registration results are verified using the calibrated NPL freeform standard.

Global ray-casting range image registration

This paper presents a novel method for pair-wise range image registration, a backbone task in world modeling, parts inspection and manufacture, object recognition, pose estimation, robotic navigation, and reverse engineering. The method finds the most suitable homogeneous transformation matrix between two constructed range images to create a more complete 3D view of a scene. The proposed solution integrates a ray casting-based fitness estimation with a global optimization method called improved self-adaptive differential evolution. This method eliminates the fine registration steps of the well-known iterative closest point (ICP) algorithm used in previously proposed methods, and thus, is the first direct global registration algorithm. With its parallel implementation potential, the ray casting-based algorithm speeds up the fitness calculation for the global optimization method, which effectively exploits the search space to find the best transformation solution. The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem. The fast, accurate, and robust results show that the proposed algorithm significantly improves on the registration problem over state-of-the-art algorithms.

Differential evolution algorithm-based range image registration for free-form surface parts quality inspection

Abstract Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection; it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

Registration of range images based on local shape features is widely adopted due to its validated effectiveness and robustness, while most existing local shape descriptors struggle to simultaneously achieve a pleasurable and balanced performance in terms of distinctiveness, robustness and time efficiency. This paper proposes a novel representation of 3D local surfaces, called multi-attribute statistics histograms (MaSH), for automatic registration of range images. MaSH comprises both spatial and geometric information characterizations. The characterization of spatial information is achieved via radial partitions in the 3D local support volume around the keypoint based on a local reference axis (LRA), creating a set of subspaces. While the encoding the shape geometry is performed by calculating statistical histograms of multiple faint correlated geometric attributes (i.e., local depth, normal deviation, and surface variation angle) for each subspace, so as to achieve information complementarity. Then, a robust rigid transformation estimation algorithm named 2-point based sample consensus with global constrain (2SAC-GC) is presented to tackle the problem of calculating an optimal transformation from the correspondence set with outliers. Finally, a coarse-to-fine registration method based on MaSH and 2SAC-GC is proposed for aligning range images. Experiments on both high-resolution (Laser Scanner) and low-resolution (Kinect) datasets report that, our method achieves a registration accuracy of 90.36% and 80.39% on the two datasets, respectively. It also exhibits strong robustness against noise and varying mesh resolutions. Furthermore, feature matching experiments show the over-all superiority of the proposed MaSH descriptor against the state-of-the-arts including the spin image, snapshots, THRIFT, FPFH, RoPS, LFSH and RCS descriptors.

Keypoints-based surface representation for 3D modeling and 3D object recognition

The three-dimensional (3D) modeling and recognition of 3D objects have been traditionally performed using local features to represent the underlying 3D surface. Extraction of features requires cropping of several local surface patches around detected keypoints. Although an important step, the extraction and representation of such local patches adds to the computational complexity of the algorithms. This paper proposes a novel Keypoints-based Surface Representation (KSR) technique. The proposed technique has the following two characteristics: (1) It does not rely on the computation of features on a small surface patch cropped around a detected keypoint. Rather, it exploits the geometrical relationship between the detected 3D keypoints for local surface representation. (2) KSR is computationally efficient, requiring only seconds to process 3D models with over 50,000 points with a MATLAB implementation. Experimental results on the UWA and Stanford 3D models dataset suggest that it can accurately perform pairwise and multiview range image registration (3D modeling). KSR was also tested for 3D object recognition with occluded scenes. Recognition results on the UWA dataset show that the proposed technique outperforms existing methods including 3D-Tensor, VD-LSD, keypoint-depth based feature, spherical harmonics and spin image with a recognition rate of 95.9%. The proposed approach also achieves a recognition rate of 93.5% on the challenging Ca'Fascori dataset compared to 92.5% achieved by game-theoretic. The proposed method is computationally efficient compared to state-of-the-art local feature methods.

An ensemble fruit fly optimization algorithm for solving range image registration to improve quality inspection of free-form surface parts

Free-form surface part inspection can be conducted by comparing an ideal design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective.

分光轴式多源图像融合系统近距离配准方法研究

分光轴式多源图像融合系统近距离配准方法研究

分光轴式多源图像融合系统近距离配准方法研究

分光轴式多源图像融合系统近距离配准方法研究

A novel 3D vorticity based approach for automatic registration of low resolution range images

This paper tackles the problem of feature matching and range image registration. Our approach is based on a novel set of discriminating three-dimensional (3D) local features, named 3D-Vor (Vorticity). In contrast to conventional local feature representation techniques, which use the vector field (i.e. surface normals) to just construct their local reference frames, the proposed feature representation exploits the vorticity of the vector field computed at each point of the local surface to capture the distinctive characteristics at each point of the underlying 3D surface. The 3D-Vor descriptors of two range images are then matched using a fully automatic feature matching algorithm which identifies correspondences between the two range images. Correspondences are verified in a local validation step of the proposed algorithm and used for the pairwise registration of the range images. Quantitative results on low resolution Kinect 3D data (Washington RGB-D dataset) show that our proposed automatic registration algorithm is accurate and computationally efficient. The performance evaluation of the proposed descriptor was also carried out on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the tasks of automatic range image registration. Reported experimental results show that the proposed local surface descriptor is robust to resolution, noise and more accurate than state-of-the-art techniques. It achieves 90% registration accuracy compared to 50%, 69.2% and 52% for spin image, 3D SURF and SISI/LD-SIFT descriptors, respectively.

A comparative study on the application of advanced bacterial foraging models to image registration

New swarm intelligence approaches as the Bacterial Foraging Optimization Algorithm (BFOA) have recently awakened a growing interest in the evolutionary computation community. This fact is due to the promising results obtained by different variants of the latter optimization method in many real-world applications. In this work we aim to take a step ahead in the development of the discipline by introducing a large amount of BFOA variants resulting from the combination of some advanced design decisions applied on Dasgupta et al.’s self-adaptive version. Our goal is thus to achieve an improved understanding of the good properties that the BFOA approach has shown in previous contributions. We will perform an extensive experimental study considering a plethora of algorithmic variants to solve a real-world problem, image registration, a well-known and complex task in computer vision. In particular, more than fifty variants are proposed and tested tackling pair-wise image registration problem instances from two different domains, namely range image registration for 3D model reconstruction and 3D medical image registration. The reported results reveal that BFOA is a versatile approach able to provide very competitive outcomes to face challenging real-world image registration problems when compared to the state-of-the-art evolutionary approaches in the field.

An Accurate and Robust Range Image Registration Algorithm for 3D Object Modeling

Range image registration is a fundamental research topic for 3D object modeling and recognition. In this paper, we propose an accurate and robust algorithm for pairwise and multi-view range image registration. We first extract a set of Rotational Projection Statistics (RoPS) features from a pair of range images, and perform feature matching between them. The two range images are then registered using a transformation estimation method and a variant of the Iterative Closest Point (ICP) algorithm. Based on the pairwise registration algorithm, we propose a shape growing based multi-view registration algorithm. The seed shape is initialized with a selected range image and then sequentially updated by performing pairwise registration between itself and the input range images. All input range images are iteratively registered during the shape growing process. Extensive experiments were conducted to test the performance of our algorithm. The proposed pairwise registration algorithm is accurate, and robust to small overlaps, noise and varying mesh resolutions. The proposed multi-view registration algorithm is also very accurate. Rigorous comparisons with the state-of-the-art show the superiority of our algorithm.

A case study of innovative population-based algorithms in 3D modeling: Artificial bee colony, biogeography-based optimization, harmony search

Deterministic or analytical methods for computing the global optima of a functional have been extensively applied in a wide range of engineering applications. Nevertheless, it is wellknown they usually lack of effectiveness when dealing with complex nonlinear optimization problems. In particular, such a shortcomings have been addressed by using approximate approaches, named metaheuristics. Among them all, those methods using a population-based scheme, e.g. the evolutionary algorithms, have been the most successful optimization strategies. Recently, innovative population-based algorithms such as ABC, BBO, and HS have arisen as promising optimization methods due to they provide a good tradeoff between design and performance when compared to other more elaborated methods. In this work, we aim to first introduce the particular design of these three cutting edge algorithms, and additionally analyse their performance when tackling a challenging real-world optimization problem. In particular, our case study of numerical optimization tackles a computer vision problem named 3D range image registration for 3D modeling tasks. Computational experiments have been conducted comparing the performance of ABC, HS, and BBO against other contributions in the state-of-the-art of 3D image registration.

Range Image Registration Using a Photometric Metric under Unknown Lighting

Based on the spherical harmonics representation of image formation, we derive a new photometric metric for evaluating the correctness of a given rigid transformation aligning two overlapping range images captured under unknown, distant, and general illumination. We estimate the surrounding illumination and albedo values of points of the two range images from the point correspondences induced by the input transformation. We then synthesize the color of both range images using albedo values transferred using the point correspondences to compute the photometric reprojection error. This way allows us to accurately register two range images by finding the transformation that minimizes the photometric reprojection error. We also propose a practical method using the proposed photometric metric to register pairs of range images devoid of salient geometric features, captured under unknown lighting. Our method uses a hypothesize-and-test strategy to search for the transformation that minimizes our photometric metric. Transformation candidates are efficiently generated by employing the spherical representation of each range image. Experimental results using both synthetic and real data demonstrate the usefulness of the proposed metric.