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![The bunny model to be repaired. a the original bunny mesh with a simple hole consists of 4964 vertices and 9900 triangle faces, while in b the filled result mesh of the algorithm [21] consists of 11,314 vertices and 22,624 triangle faces](publication/307627966/figure/fig5/AS:402975966154759@1473088365290/The-bunny-model-to-be-repaired-a-the-original-bunny-mesh-with-a-simple-hole-consists-of.png)
The bunny model to be repaired. a the original bunny mesh with a simple hole consists of 4964 vertices and 9900 triangle faces, while in b the filled result mesh of the algorithm [21] consists of 11,314 vertices and 22,624 triangle faces
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![Figure 5: The hole filling results between the original Meshfix [25]...](publication/307627966/figure/fig3/AS:402975446061056@1473088241088/The-hole-filling-results-between-the-original-Meshfix-25-left-and-modified-MeshFix_Q320.jpg)
The surface reconstruction of 3D objects has attracted more and more attention for its widespread application in many areas, such as computer science, cultural heritage restoration, medical facilities, entertainment. However, due to occlusion, reflectance, the scanning angle, raw data preprocessing, it is inevitable to lose some point data, which l...
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The procedural generation of data sets for empirical algorithm validation and deep learning tasks in the area of primitive-based geometry is cumbersome and time-consuming while ready-to-use data sets are rare. We propose a new and highly flexible framework based on Evolutionary Computing that is able to create primitive-based abstractions of existi...
Citations
... However, in the present work, we discuss only surface restoration [16][17][18][19][20][21][22][23]. We refer interested readers to a survey of the relevant literature [24]. In [16], volumetric diffusion method was proposed as an algorithm to restore a complex surface. ...
In this study, we propose a restoration algorithm for distorted objects using a curvature-driven flow. First, we capture the convex-hull shaped contour of the distorted object using the mean curvature flow. With the supplemented mass on the captured feature, we evolve the constraint mean curvature flow to a steady state, preserving the non-distorted region. With respect to the mass, we select a restorative shape by considering the square of the curvature derivative. The Allen–Cahn and Cahn–Hilliard equations are applied to the generated restored image from the implicit curvature motions represented by the order parameter. We impose the Dirichlet boundary condition for the order parameter and the Neumann boundary for the chemical potential to fix the feature and to inherit the mass conservation, respectively. We provided examples of the restoration of half-circle and parentheses-shaped objects to reconstruct a circle shape.
... With the progression of 3D scanning technology, significant investigation into Hole-Filling algorithms has been conducted to remedy the occurrence of holes generated by diverse causes within 3D models. These algorithms are primarily bifurcated into two major categories, namely 'Point Cloud-based methods' and 'Mesh-based methods' as shown in Figure 1 [12]. Initially, the focus was directed towards 'Point Cloud-based methods', where raw point data of scanned objects were utilized as input. ...
... Within 'Volume-based methods', the input mesh is transformed into volume representation, followed by the application of algorithms such as Volumetric Diffusion, Octree Grid, and Oriented Voxel Diffusion, to ensure reconstruction without defects [12,13]. In contrast, 'Surfacebased methods' operate solely on the input hole's information or its surrounding information to fashion patches for the hole. ...
In the realm of Augmented Reality (AR) within mobile gaming, the planes recognized by depth sensors delineate the space for content implementation, thereby constraining the scope of representation. Errors in these recognized planes may inhibit the progression of mobile AR. A method is explored that utilizes 'Meshing' facilitated by Unity, generating meshes corresponding to physical space and enabling expansion of content space beyond mere planes. This approach, although promising, is contingent on the depth sensor, leading to the creation of holes beyond the sensor's reach. These holes present a critical issue, allowing game objects to escape. To address this challenge, an algorithm is proposed that consists of two main components: 'Hole-Finding' and 'Hole-Filling'. In 'Hole-Finding', real holes are identified by the calculation of the direction of each loop. Subsequently, 'Hole-Filling' computes the centroid-vertex of each hole and employs it for the hole-filling process. A real-time hole-filling performance with only a 7 μsec degradation was observed, heralding a significant step towards mitigating this problem within AR content. This investigation contributes a novel solution to a crucial technical obstacle, thereby enhancing the functionality and potential of AR in mobile gaming.
... For example, when scanning complex geometric objects, it is always difficult to scan every point of the underlying object [33]. The scanned geometry usually contains various levels of holes and missing geometries, making it critical to develop high-quality geometry completion techniques [61,10,68,13,1,24,18]. Geometry completion is also used in interactive shape modeling [7], as a way to suggest additional content to add to a partial 3D object/scene. ...
... Early geometry completion techniques focus on hole filling [61,10,68,13,1,24,18]. These techniques rely on the assumption that the missing regions are simple surface patches and can be filled by smoothly extending hole regions. ...
... 9: Architecture of the feature encoder in the retrieval learning part. The input is a patch with size (18,18,18). The output is a feature vector with size 128. ...
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative methods lack the training data and representation capacity to synthesize plausible, fine-grained details with complex geometry and topology. Our key insight is to copy and deform patches from the partial input to complete missing regions. This enables us to preserve the style of local geometric features, even if it drastically differs from the training data. Our fully automatic approach proceeds in two stages. First, we learn to retrieve candidate patches from the input shape. Second, we select and deform some of the retrieved candidates to seamlessly blend them into the complete shape. This method combines the advantages of the two most common completion methods: similarity-based single-instance completion, and completion by learning a shape space. We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps. Experimental results show our approach considerably outperforms baselines across multiple datasets and shape categories. Code and data are available at https://github.com/GitBoSun/PatchRD.
... For scientific computing purposes, 3D models for anatomic structures must be spatially compliant to ensure accurate calculation of spatial relationships and measurements. However, it is inevitable to lose vertices or edges due to the occlusion, reflectance or raw data preprocessing, rendering holes in the surface and non-manifold meshes [14]. For example, the mesh in Figure 3a is divided into two parts, i.e., the renal pyramid and the renal papilla, according to biological functionalities and structures. ...
... Due to the differences of data representation, there are two categories of current hole filling algorithms [14]: point cloud-based methods and mesh-based methods. Point cloud-based methods were developed in earlier years, while mesh-based methods gain more attention and play an important role with the rapid development of computer graphics. ...
... Accordingly, exploring the way to detect the holes in the point cloud and distinguish between 'true' holes and 'false' holes is an important step to realize object defect detection, and it is also a challenging problem that this paper aims to address. The commonly used point cloud hole detection algorithms are all based on scattered point cloud data or a mesh model, such as the triangular mesh discrimination method [11,12], maximum angle recognition method, and half-disc recognition method [13], and have been widely used in some specific cases. However, in fact, no algorithm is absolutely universal. ...
... Specifically, through triangulating, the boundary edges that belong to only one triangle are extracted. Then these boundary edges are connected end to end to form a closed loop, thereby realizing the detection of the hole boundary of the point cloud [11,12]. ...
In the process of defect detection, there could be interference factors such as poor image quality and missing point cloud data as a result of the complexity of the acquisition environment. Certain limitations can be found relying solely on point cloud data processing or image feature detection. Therefore, this paper tries a more intuitive and effective exploration. Firstly, an algorithm named hole boundary points detection of point cloud based on multi-scale principal component analysis is proposed, which can achieve the preliminary detection of hole boundary points while calculating the normal vector of each point in the point cloud. Then the boundary contour of each hole is constructed by a polygon growth algorithm. Finally, we use the complementary information of three-dimensional (3D) point cloud and two-dimensional (2D) image to explore the origin of holes and realize the "true" and "false" classification of holes. The experimental results show that our algorithm can successfully detect point cloud holes and can also distinguish them from object defects, providing data support for subsequent holes filling and defect measurement.
... This is followed by an unusual hole in the surface of the Tara statue's right hand. Several hole-filling algorithms have been suggested, including Guo Xiaoyuan's algorithm was for simple hole-filling [6] and Wang Jianning's algorithm [7] for complex holes with an island, covering only half of our problem, which consists of hole as well as gaps. Another study of hole-filling was suggested by Amitish [8], which can only fill simple holes on highly variable surfaces. ...
... Another study of hole-filling was suggested by Amitish [8], which can only fill simple holes on highly variable surfaces. A review of these related studies [6,7,8] showed that their methods need modification. Our suggested method is a uniform B-spline surface [9]. ...
Researchers assume that the bodies of several Tara statues may have been molded from the same template. Therefore, researchers are eager to study the shape similarities between the Tara statues. Motivated by the archaeologist's requirements, this study presents an efficient approach to evaluate shape similarities between point clouds of two different Tara statues right-arm models. The evaluation approach consists of two main stages. The first is the reconstruction of individual body parts without decoration using a B-spline surface approximation technique. In previous reconstruction process, the surrounding surface selection parameter was manually specified. If the manually applied parameter is inadequate, it reduces the accuracy of the surface approximation and hole-filling, resulting in reduced accuracy of the evaluation result. Therefore, in this study, the surrounding surface selection parameter is automatically determined based on the Golden-section-search algorithm. The second stage evaluates the shape similarities between two different point clouds. The evaluation method considers a simple point-to-surface distance metric method with overlapping-surface detection and when compared with generic point-to-point distance metric method, the evaluation results proved that the proposed methodology was reliable and effective for this application. The approximation accuracy affects the evaluation result when the reconstruction method fills the gap by adding new points through the approximated surface. Therefore, in this study, the shape similarities were investigated between the two arm models with holes, immediately after separating the decoration parts.
... Due to the differences of data representation, current hole filling algorithms [17] can be divided into two categories: point cloud-based methods and mesh-based methods. Point cloud-based methods were developed in earlier years, while mesh-based methods gain more attention and play an important role with the rapid development of computer graphics. ...
A bstract
The human body is made up of about 37 trillion cells (adults). Each cell has its own unique role and is affected by its neighboring cells and environment. The NIH Human BioMolecular Atlas Program (HuBMAP) aims at developing a 3D atlas of human body consisting of organs, vessels, tissues to singe cells with all 3D spatially registered in a single 3D human atlas using tissues obtained from normal individuals across a wide range of ages. A critical step of building the atlas is to register 3D tissue blocks in real-time to the right location of a human organ, which itself consists of complex 3D substructures. The complexity of the 3D organ model, e.g., 35 meshes for a typical kidney, poses a significant computational challenge for the registration. In this paper, we propose a comprehensive framework TICKET ( TI ssue blo CK r E gis T ration) for tissue block registration for 3D human atlas, including (1) 3D mesh pre-processing, (2) spatial queries on intersection relationship and (3) intersection volume computation between organs and tissue blocks. To minimize search space and computation cost, we develop multi-level indexing on both the anatomical structure level and mesh level, and utilize OpenMP for parallel computing. Considering cuboid based shape of the tissue block, we propose an efficient voxelization-based method to estimate the intersection volume. Our experiments demonstrate that the proposed framework is practical and efficient. TICKET is now integrated into the HuBMAP CCF registration portal [1].
... For example, when scanning complex geometric objects, it is always difficult to scan every point of the underlying object [33]. The scanned geometry usually contains various levels of holes and missing geometries, making it critical to develop high-quality geometry completion techniques [61,10,68,13,1,24,18]. Geometry completion is also used in interactive shape modeling [7], as a way to suggest additional content to add to a partial 3D object/scene. ...
... Early geometry completion techniques focus on hole filling [61,10,68,13,1,24,18]. These techniques rely on the assumption that the missing regions are simple surface patches and can be filled by smoothly extending hole regions. ...
... 9: Architecture of the feature encoder in the retrieval learning part. The input is a patch with size (18,18,18). The output is a feature vector with size 128. ...
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative methods lack the training data and representation capacity to synthesize plausible, fine-grained details with complex geometry and topology. Our key insight is to copy and deform patches from the partial input to complete missing regions. This enables us to preserve the style of local geometric features, even if it drastically differs from the training data. Our fully automatic approach proceeds in two stages. First, we learn to retrieve candidate patches from the input shape. Second, we select and deform some of the retrieved candidates to seamlessly blend them into the complete shape. This method combines the advantages of the two most common completion methods: similarity-based single-instance completion, and completion by learning a shape space. We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps. Experimental results show our approach considerably outperforms baselines across multiple datasets and shape categories. Code and data are available at https://github.com/GitBoSun/PatchRD.
... As an effective method of data mining, cluster analysis has the characteristics of unsupervised learning, which can find hidden behavior patterns in a large number of irregular and noisy e-commerce user behavior data, presenting the characteristics of hidden e-commerce user behavior data, and providing theoretical support for people's decision and judgment. However, the traditional K-means clustering algorithm has the defects of large amount of calculation and long time to obtain the optimal solution [14]. ...
In order to reduce the time for customers to select commodities they are interested in, improve the purchase efficiency, improve the success rate of sales of merchants, and create greater economic benefits for enterprises and merchants, this project collects information and data of e-commerce users, using neural network model to analyze and mine data characteristics and shopping records of e-commerce users. According to the analysis results, a user commodity recommendation system based on e-commerce is implemented by using data mining technology. Through the combination of database technology, the transaction and browsing data generated in the process of e-commerce transactions are collected. The collected data is preformatted and used as the input of data mining. Then, it uses data mining technology to mine and analyze the commodities that users are interested in, makes matching according to the types of commodities, and recommends the commodities that users are interested in under a given scene according to the established prediction model. By combining fuzzy clustering with collaborative filtering algorithm, this paper recommends the products that users are interested in, which are mined from historical data and commodity information.
... Both BP network and GA are suitable for solving nonlinear problems, but they have their own advantages and disadvantages: BP network has superior self-learning, adaptive, self-organizing, and large-scale parallel processing capabilities, but its error function is square and sensitive to the initial weight setting of the network, which is easy to cause oscillation effect [14,15]. By using GA to optimize the BP and combining the advantages of GA, the genetic algorithm is selected, crossed, and mutated by genetic operators to generate individual optimal species, find the initial weight and threshold of the optimal solution of BP neural network, assign values, and then train the BP neural network model to finally obtain the global optimal prediction value. ...
Due to the generality and particularity of Internet financial risks, it is imperative for the institutions involved to establish a sound risk prevention, control, monitoring, and management system and timely identify and alert potential risks. Firstly, the importance of Internet financial risk monitoring and evaluation is expounded. Secondly, the basic principles of backpropagation (BP) neural network, genetic algorithm (GA), and GABP algorithms are discussed. Thirdly, the weight and threshold of the BP algorithm are optimized by using the GA, and the GABP model is established. The financial risks are monitored and evaluated by the Internet financial system as the research object. Finally, GABP is further optimized by the simulated annealing (SA) algorithm. The results show that, compared with the calculation results of the BP model, the GABP algorithm can reduce the number of BP training, has high prediction accuracy, and realizes the complementary advantages of GA and BP neural network. The GABP network optimized by simulated annealing method has better global convergence, higher learning efficiency, and prediction accuracy than the traditional BP and GABP neural network, achieves better prediction effect, effectively solves the problem that the enterprise financial risk cannot be quantitatively evaluated, more accurately assesses the size of Internet financial risk, and has certain popularization value in the application of Internet financial risk prediction.
