Geometric Processing Research Articles

Open-Source Tool for Transforming CityGML Levels of Detail

Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

Optimization of low-power femtosecond laser trepan drilling by machine learning and a high-throughput multi-objective genetic algorithm

The geometric quality (taper), recast layer, and processing efficiency of micro-holes are important issues in femtosecond laser trepan drilling (LTD). Although one-step drilling based on low-power femtosecond LTD may be an ideal drilling method, its disadvantages such as processing time and taper quality still need to be improved. To address these issues, machine learning was successfully applied to establish an accurate predictive model for the femtosecond LTD process. Based on the machine learning model, the femtosecond LTD results for a given parameter set were quickly and accurately obtained, avoiding a large number of complex experiments and characterization requirements. Subsequently, through the combination of our established optimal machine learning predictive model and a high-throughput genetic algorithm, optimized solutions were quickly and successfully designed for a wide range of process spaces. Finally, the reliability of the optimized process was verified by experiments. The combination of machine learning and a high-throughput optimization algorithm provided an efficient and low-cost solution for the optimization of complex laser processing technology.

GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks

Mesh denoising is an essential geometric processing step for raw meshes generated by 3D scanners and depth cameras. It is intended to remove noise while preserving surface intrinsic features of the underlying model. Existing mesh denoising wisdoms of (1) updating mesh vertices directly (referred to as one-step mesh denoising in the spatial domain) or (2) normal filtering followed by vertex updating (referred to as two-step mesh denoising in the normal domain) seldom consider the correlation between vertex updating and normal filtering. This paper proposes a novel end-to-end geometry-aware dual-graph neural network, called GeoBi-GNN, to perform denoising in spatial and normal domains simultaneously. For the first time, we optimize both positions and normals (i.e., dual domains) in a unified framework of GNN, and show the powerful inter-coordination between the dual domains. GeoBi-GNN fully excavates the native dual-graph structure in the mesh, and creates two graph structures for the spatial noise and the normal noise respectively through the adjacency relationship between vertices and surfaces. We design each GNN as a three-layer U-Net architecture to gradually extract multi-scale features from the input graphs. In addition, a specific graph pooling layer with a cascaded weight estimation strategy is designed to improve the robustness and denoising effect. Due to the intuitive relation between the mesh connectivity and the dual graphs, the proposed method is simple to implement. Comprehensive experiments exhibit that the proposed method is significantly superior to the state-of-the-arts of mesh denoising, especially for the large-scale noise and the complex real scans. Our code is available at https://github.com/zhangyk18/GeoBi-GNN.

Multistage Tool Path Optimisation of Single-Point Incremental Forming Process.

Single-point incremental forming (SPIF) is a flexible technology that can form a wide range of sheet metal products without the need for using punch and die sets. As a relatively cheap and die-less process, this technology is preferable for small and medium customised production. However, the SPIF technology has drawbacks, such as the geometrical inaccuracy and the thickness uniformity of the shaped part. This research aims to optimise the formed part geometric accuracy and reduce the processing time of a two-stage forming strategy of SPIF. Finite element analysis (FEA) was initially used and validated using experimental literature data. Furthermore, the design of experiments (DoE) statistical approach was used to optimise the proposed two-stage SPIF technique. The mass scaling technique was applied during the finite element analysis to minimise the computational time. The results showed that the step size during forming stage two significantly affected the geometrical accuracy of the part, whereas the forming depth during stage one was insignificant to the part quality. It was also revealed that the geometrical improvement had taken place along the base and the wall regions. However, the areas near the clamp system showed minor improvements. The optimised two-stage strategy successfully decreased both the geometrical inaccuracy and processing time. After optimisation, the average values of the geometrical deviation and forming time were reduced by 25% and 55.56%, respectively.

Comparison of Two Parallel Offsetting Algorithms Free from Conflicts Between Threads

Offset computation for expanding a polyhedral object by an offset radius is a fundamental geometric process frequently used in manufacturing applications. This process combined with the triple-dexel representation solid model has become popular because of its robustness and compatibility with parallel processing using a graphics processing unit (GPU). In parallel geometric processing, conflicts between threads must be avoided. Thus, we propose a novel parallel offsetting algorithm free from conflicts between threads. The triple-dexel model is a combination of x-, y-, and z-axis-aligned dexel models. Each dexel model is defined based on an orthogonal grid given on a coordinate plane. We subdivide the grid into several sub-grids of a fixed size in advance. For each sub-grid, a block of GPU threads is assigned. As each GPU thread always processes different dexel elements from the other threads in this method, no conflict occurs. Our research group has previously presented a parallel offset computation algorithm for a polyhedral solid model that also uses a triple-dexel representation model and a GPU. In the previous algorithm, the surface polygons of the model are classified into several groups in advance. The parallel offset computation of multiple polygon groups is realized by selecting groups of polygon in which the offset processing of the polygons does not affect one another. This selection process is time-consuming. Computational experiments were performed to analyze the performance difference between the current algorithm and our previous algorithm. In our experiments, the current algorithm achieved speedups of 1.4 times to 3.2 times compared to our previous offsetting algorithm.

Application of Random Region Augmentation Algorithm in Deep Learning

In the field of computer vision, the collection and sorting of image data is the core driving force. However, the current data collection work cannot perfectly collect the image data of each actual landing scene. The purpose of the data augmentation algorithm is to increase the diversity of the data set and improve the robustness of the model. Traditional data augmentation methods include geometric augmentation and color augmentation, mainly including flipping, rotating, cropping, translation, stretching, zooming, adding noise, blurring, Dropout, Cutout, color jittering. Traditional data augmentation methods have certain limitations, and the effect is not obvious. Based on the idea of Cutout algorithm, this paper proposes the RRA augmentation algorithm, which divides four quadrant regions in the image, and randomly selects the ROI region in each region, and is different from the Cutout algorithm directly discarding the region, but randomizing the region Enhance the color, and finally do geometric augmentation processing on the overall image. Compared with the original single data augmentation operation, the algorithm improves precision by 7%, and recall improves by 7%.

Geodesic Hermite Spline Curve on Triangular Meshes

Curves on a polygonal mesh are quite useful for geometric modeling and processing such as mesh-cutting and segmentation. In this paper, an effective method for constructing C1 piecewise cubic curves on a triangular mesh M while interpolating the given mesh points is presented. The conventional Hermite interpolation method is extended such that the generated curve lies on M. For this, a geodesic vector is defined as a straightest geodesic with symmetric property on edge intersections and mesh vertices, and the related geodesic operations between points and vectors on M are defined. By combining cubic Hermite interpolation and newly devised geodesic operations, a geodesic Hermite spline curve is constructed on a triangular mesh. The method follows the basic steps of the conventional Hermite interpolation process, except that the operations between the points and vectors are replaced with the geodesic. The effectiveness of the method is demonstrated by designing several sophisticated curves on triangular meshes and applying them to various applications, such as mesh-cutting, segmentation, and simulation.

Control of mobile equipment for the processing of marine shaft lines

The purpose of the research is to develop solutions for mobile equipment control in the processing of marine shaft lines; to solve the issues of geometric accuracy of processing; to create a corrective program to address the issues of accuracy of the geometric parameters processing; to give reasons for the application of linear electrohydraulic step drive (LEHSD) in mobile equipment control. The analysis of schemes and mathematical dependencies for calculating the trajectory of the tool movement of a mobile machine when processing shaft lines is carried out; the choice of the optimal zone for finishing the shaft line, taking into account the beats. The object of the study is a ship shaft line. The paper presents an original scheme of rotation of the shaft line section, as well as schemes for changing the position of the shaft line during processing; justifies the possibility of using a linear electrohydraulic step drive (LEHSD) with a corrective program for processing taking into account automatic measurements and a specified error.

Graph-Based Shape Analysis for Heterogeneous Geometric Datasets: Similarity, Retrieval and Substructure Matching

Practically all existing shape analysis and processing algorithms have been developed for specific geometric representations of 3D models. However, the product development process always involves a large number of often incompatible geometric representations tailored to specific computational tasks that take place during this process. Consequently, a substantial effort has been expended to develop robust geometric data translation and conversion algorithms, but the existing methods have well known limitations.The Maximal Disjoint Ball Decomposition (MDBD) was recently defined as a unique and stable geometric construction and used to define universal shape descriptors based on the contact graph associated with MDBD. In this paper, we demonstrate that by applying graph analysis tools to MDBD in conjunction with graph convolutional neural networks and graph kernels, one can effectively develop methods to perform similarity, retrieval and substructure matching from geometric models regardless of their native geometric representation. We show that our representation-agnostic approach achieves comparable performance with state-of-the-art geometric processing methods on standard yet heterogeneous benchmark datasets while supporting all valid geometric representations.

Imperceptible–Visible Watermarking to Information Security Tasks in Color Imaging

Digital image watermarking algorithms have been designed for intellectual property, copyright protection, medical data management, and other related fields; furthermore, in real-world applications such as official documents, banknotes, etc., they are used to deliver additional information about the documents’ authenticity. In this context, the imperceptible–visible watermarking (IVW) algorithm has been designed as a digital reproduction of the real-world watermarks. This paper presents a new improved IVW algorithm for copyright protection that can deliver additional information to the image content. The proposed algorithm is divided into two stages: in the embedding stage, a human visual system-based strategy is used to embed an owner logotype or a 2D quick response (QR) code as a watermark into a color image, maintaining a high watermark imperceptibility and low image-quality degradation. In the exhibition, a new histogram binarization function approach is introduced to exhibit any watermark with enough quality to be recognized or decoded by any application, which is focused on reading QR codes. The experimental results show that the proposed algorithm can embed one or more watermark patterns, maintaining the high imperceptibility and visual quality of the embedded and the exhibited watermark. The performance evaluation shows that the method overcomes several drawbacks reported in previous algorithms, including geometric and image processing attacks such as JPEG and JPEG2000.

Blind robust image watermarking based on adaptive embedding strength and distribution of quantified coefficients

The image watermarking technique is a reliable method for protecting digital image rights. The most widely used digital imaging format is the JPEG (joint photographic expert’s group). This paper proposes a novel reversible robust watermarking scheme based on the JPEG image. In JPEG image, quantified Discrete Cosine Transform (DCT) coefficients with varying frequency provide varied capacities and embedding deformities. To minimize total distortion for the watermarked image, we adaptively select embedding strengths and coefficients from effective frequency selection that produce less embedding distortions. Then, by weighting the difference between the quantified DCT coefficient and the DCT coefficient on the block, an advanced block selection strategy is used to the corresponding distortion, which are produced from the whole quantified alternating current (AC) shift capacity and half of the insertion capacity. The experimental results show that the proposed method can maintain good visual quality while requiring little bitstream expansion and is robust to geometrical and image processing attacks.

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

PurposeThe purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments.Design/methodology/approachThe process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized.FindingsThe proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches.Research limitations/implicationsThe methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions.Originality/valueThe paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Deep learning for processing and analysis of remote sensing big data: a technical review

ABSTRACT In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deep-learning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, land-use/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.

Association between symbol digit modalities test and regional cortex thickness in young adults with relapsing-remitting multiple sclerosis

BackgroundMultiple sclerosis (MS) is a demyelinating disease of the central nervous system, predominating within young adults. Cognitive disorders are common in MS and have are associated with several Magnetic Resonance Imaging (MRI) markers, especially brain atrophy. Many have found the symbol digit modalities test (SDMT) to be the most sensitive individual cognitive measure relevant to MS. However, the relationship between SDMT and regional brain cortex thickness in young adults with relapsing-remitting multiple sclerosis (YA-RRMS) has been little explored. The purpose of this study was to investigate the association between the SDMT and regional cortex thickness in YA-RRMS by FreeSurfer, which is an automatic brain structure segmentation method. MethodTwenty-eight YA-RRMS patients (18–35 years old) were enrolled in the present study. Informed consent and information including gender, age, disease duration, number of relapses, annual relapse rate was collected from all patients. Clinical cognitive evaluations (SDMT and auditory verbal learning test (AVLT)) and daily performance: activities of daily living (ADL) were assessed in the present study. MRI scans were performed at the Institute of Neurosurgery of Tiantan Hospital. Twenty-eight matched healthy controls (HC) MRI data were obtained from Tiantan Hospital database. Data on thirty-four points of bilateral cortical structure thickness using statistically defined brain regions-of-interest from FreeSurfer were obtained from all participants. ResultsPatients with RRMS exhibited extensively thinner cerebellar cortex compared with HC. SDMT scores were significantly correlated with AVLT subentries (IM, immediate memory; DRM, delayed recall memory; LTRM, long-term recognition memory) in YA-RRMS patients (P < 0.05). SDMT was strongly correlated with regional cortex thickness differences of the right temporal pole (r = 0.68) and bilateral parahippocampal areas (right r = 0.62; left r = 0.60), and moderately correlated with regional cortex thickness differences including the left superior temporal and right insula (r = 0.57 and 0.56, respectively) in YA-RRMS patients. ConclusionThe present study has shown the SDMT is strongly correlated with selected cortex regions including the bilateral parahippocampal area and the right temporal pole which are involved in geometric structures processing.

Medical Image Authentication by Combining Hash Signature and Watermarking Based on Frequency Domains

Medical images are transmitted to other remote places through the E-healthcare system. The protection of medical images is very crucial. Medical images need to be protected against any modification which the attacker may do through the unsecured channel. It is necessary for inspecting the integrity of the ROI (Region of Interest) of the received medical image prior to the adoption of any diagnostic decision to avoid a misdiagnosis. This paper aimed to confirm the integrity and authenticity of medical images by combining the hash signature with the watermarking technique based on the frequency domains. At first, the medical image is divided into ROI and RONI (Region of Non-Interest). The SLT and DCT transformations are employed together to extract the essential features set from ROI and then combined with EPR (Electronic Patient Registration) to produce a watermark, and then it is encoded by a chaotic map with a secret key to provide a signature. On the other hand, the RONI is mapping into randomly subblocks based on the linear system, and then the Signature is embedded in the chosen block using DWT. The hash code of the recovered image and EPR will be compared with the extracted watermark for integrity and authenticity. The experimental results demonstrate the watermark’s robustness against many of the more aggressive and geometric signal processing distortions without affecting the quality of the original medical image. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value larger than 0.90 under all types of attacks.

DROACOR® REFLECTANCE RETRIEVAL FOR HYPERSPECTRAL MINERAL EXPLORATION USING A GROUND-BASED ROTATING PLATFORM

Abstract. The acquisition of imaging spectroscopy data from ground based rotating stages is a novel approach which is more and more used in open pit mines for prospection and controlling. The special radiometric situation of such data sets asks for new processing approaches for geometric processing as well as for reflectance retrieval. Herein, a new method for atmospheric correction and relative reflectance retrieval is presented which is optimized for the special horizontal scanning situation by hyperspectral instruments from rotating ground based platforms. The method is implemented within the recently developed drone atmospheric correction framework DROACOR®. It combines a physical inversion of a radiative transfer code with semi-empirical correction approaches for illumination and spectral absorption. Sample results from two open pit mines show that mineral detection after reflectance retrieval allows the discrimination of iron minerals and that the retrieved spectra are well comparable to standard library spectra.

Advanced soft robot modeling in ChainQueen

AbstractWe present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

Algebra of Technological Process

A formal apparatus for modeling the structure of the technological process of mechanical processing based on the algebra of design and technological elements is presented. Design and technological element (manufacturing feature) is considered as a set of geometric processing area and the tool trajectory applied to it, set by a set of technological parameters. Algebra includes an addition operation (adding an element to the process structure) and a multiplication operation (merging elements). The set of processing elements forms an associative and generally noncommutative algebraic group. The possibility of using algebra for analysis and synthesis of technological process structures is shown

GeoGraph

In many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the Geo- Graph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and b-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing.

Stochastic Single Machine JIT Scheduling with Geometric Processing Times and Due Dates

In this paper, we investigate a static stochastic single machine JIT scheduling problem in which the jobs’ processing times are stochastically independent and follow geometric distributions whose mean is provided, due dates are geometrically distributed with a common mean, and both the unit penalty of earliness/tardiness and the fixed penalty of earliness/tardiness are deterministic and different. The objective is to minimize the expected total penalties for quadratic earliness, quadratic tardiness, and early and tardy jobs. We prove that the optimal schedule to minimize this problem is V-shaped with respect to the ratio of mean processing time to unit tardiness penalty under the specific condition. Also, we show a special case and two theorems related to this JIT scheduling problem under specific situations where the optimal solutions exist. Finally, based on the V-shaped characteristic, a dynamic programming algorithm is designed to achieve an optimal V-shaped schedule in pseudopolynomial time.