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We present a framework that folds man-made objects in a structure-aware manner for space-conserving storage and transportation. Given a segmented 3D mesh of a man-made object, our framework jointly optimizes for joint locations, the folding order, and folding angles for each part of the model, enabling it to transform into a spatially efficient configuration while keeping its original functionality as intact as possible. That is, if a model is supposed to withstand several forces in its initial state to serve its functionality, our framework places the joints between the parts of the model such that the model can withstand forces with magnitudes that are comparable to the magnitudes applied on the unedited model. Furthermore, if the folded shape is not compact, our framework proposes further segmentation of the model to improve its compactness in its folded state. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient method to eliminate the obtuse triangles for high quality 2D mesh generation. Given an initialization (e.g., from Centroidal Voronoi Tessellation—CVT), a limited number of point insertions and removals are performed to eliminate obtuse or small angle triangles. A mesh smoothing and optimization step is then applied. These steps are repeated till a desired good quality mesh is reached. We tested our algorithm on various 2D polygonal domains and verified that our algorithm always converges after inserting a few number of new points, and generates high quality triangulation with no obtuse triangles. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple yet effective method to generate 3D-conforming tetrahedral meshes from closed 2-manifold surfaces. Our approach is inspired by recent work on maximal Poisson-disk sampling (MPS), which can generate well-distributed point sets in arbitrary domains. We first perform MPS on the boundary of the input domain, we then sample the interior of the domain, and we finally extract the tetrahedral mesh from the samples by using 3D Delaunay or regular triangulation for uniform or adaptive sampling, respectively. We also propose an efficient optimization strategy to protect the domain boundaries and to remove slivers to improve the meshing quality. We present various experimental results to illustrate the efficiency and the robustness of our proposed approach. We demonstrate that the performance and quality (e.g., minimal dihedral angle) of our approach are superior to current state-of-the-art optimization-based approaches. [ABSTRACT FROM AUTHOR]

The patch layout of 3D surfaces reveals the high-level geometric and topological structures. In this paper, we study the patch layout computation by detecting and enclosing feature loops on surfaces. We present a hybrid framework which combines several key ingredients, including feature detection, feature filtering, feature curve extension, patch subdivision and boundary smoothing. Our framework is able to compute patch layouts through concave features as previous approaches, but also able to generate nice layouts through smoothing regions. We demonstrate the effectiveness of our framework by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Poisson-disk sampling is one of the fundamental research problems in computer graphics that has many applications. In this paper, we study the problem of maximal Poisson-disk sampling on mesh surfaces. We present a simple approach that generalizes the 2D maximal sampling framework to surfaces. The key observation is to use a subdivided mesh as the sampling domain for conflict checking and void detection. Our approach improves the state-of-the-art approach in efficiency, quality and the memory consumption. [ABSTRACT FROM AUTHOR]

In this paper, we present a simple yet efficient algorithm for triangulating a 2D input domain containing a Poisson-disk sampled point set. The proposed algorithm combines a regular grid and a discrete clustering approach to speedup the triangulation. Moreover, our triangulation algorithm is flexible and performs well on more general point sets such as adaptive, non-maximal Poisson-disk sets. The experimental results demonstrate that our algorithm is robust for a wide range of input domains and achieves significant performance improvement compared to the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract: We present a framework for the automatic disassembly of 3D man-made models and the illustration of the disassembly process. Given an assembled 3D model, we first analyze the individual parts using sharp edge loops and extract the contact faces between each pair of neighboring parts. The contact faces are then used to compute the possible moving directions of each part. We then present a simple algorithm for clustering the sets of the individual parts into meaningful sub-assemblies, which can be used for a hierarchical decomposition. We take the stability of sub-assemblies into account during the decomposition process by considering the upright orientation of the input models. Our framework also provides a user-friendly interface to enable the superimposition of the constraints for the decomposition. Finally, we visualize the disassembly process by generating an animated sequence. The experiments demonstrate that our framework works well for a variety of complex models. [Copyright &y& Elsevier]

Image and video inpainting is a classic problem in computer vision and computer graphics, aiming to fill in the plausible and realistic content in the missing areas of images and videos. With the advance of deep learning, this problem has achieved significant progress recently. The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting. Specifically, we sort existing methods into different categories from the perspective of their high-level inpainting pipeline, present different deep learning architectures, including CNN, VAE, GAN, diffusion models, etc., and summarize techniques for module design. We review the training objectives and the common benchmark datasets. We present evaluation metrics for low-level pixel and high-level perceptional similarity, conduct a performance evaluation, and discuss the strengths and weaknesses of representative inpainting methods. We also discuss related real-world applications. Finally, we discuss open challenges and suggest potential future research directions. [ABSTRACT FROM AUTHOR]

Abstract: The Voronoi diagram is a fundamental geometric structure widely used in various fields, especially in computer graphics and geometry computing. For a set of points in a compact domain (i.e. a bounded and closed 2D region or a 3D volume), some Voronoi cells of their Voronoi diagram are infinite or partially outside of the domain, but in practice only the parts of the cells inside the domain are needed, as when computing the centroidal Voronoi tessellation. Such a Voronoi diagram confined to a compact domain is called a clipped Voronoi diagram. We present an efficient algorithm to compute the clipped Voronoi diagram for a set of sites with respect to a compact 2D region or a 3D volume. We also apply the proposed method to optimal mesh generation based on the centroidal Voronoi tessellation. [Copyright &y& Elsevier]

Abstract: We present a new variational method for mesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both and metrics from a triangle to a quadric surface. The Lloyd iteration is used to minimize the energy function, which repeatedly interleaves between mesh partition and quadric surface fitting. We also integrate feature-based and simplification-based techniques in the segmentation framework, which greatly improve the performance. The advantages of our algorithm are demonstrated by comparing with the state-of-the-art methods. [Copyright &y& Elsevier]

Recent advancements in shrink-wrapping-based mesh approximation have shown tremendous advantages for non-manifold defective meshes. However, these methods perform unsatisfactorily when maintaining the regions with sharp features and rich details of the input mesh. We propose an adaptive shrink-wrapping method based on the recent Alpha Wrapping technique, offering improved feature preservation while handling defective inputs. The proposed approach comprises three main steps. First, we compute a new sizing field with the capability to assess the discretization density of non-manifold defective meshes. Then, we generate a mesh feature skeleton by projecting input feature lines onto the offset surface, ensuring the preservation of sharp features. Finally, an adaptive wrapping approach based on normal projection is applied to preserve the regions with sharp features and rich details simultaneously. By conducting experimental tests on various datasets including Thingi10k, ABC, and GrabCAD, we demonstrate that our method exhibits significant improvements in mesh fidelity compared to the Alpha Wrapping method, while maintaining the advantage of manifold property inherited from shrink-wrapping methods. • We propose an adaptive shrink-wrapping method that generates watertight, manifold and feature-preserving triangle meshes. • Introducing an adaptive sizing field computation method for defective meshes. • Utilize feature skeleton and normal projection for feature-preserving. • Our method achieves a balance between time efficiency and mesh fidelity. [ABSTRACT FROM AUTHOR]

Molecular surface mesh generation is a prerequisite for using the boundary element method (BEM) and finite element method (FEM) in implicit-solvent modeling. Molecular surface meshes typically have small angles, redundant vertices, and low-quality elements. In the implicit-solvent modeling of biomolecular systems it is usually required to improve the mesh quality and eliminate low-quality elements. Existing methods often fail to efficiently remove low-quality elements, especially in complex molecular meshes. In this paper, we propose a mesh refinement method that smooths the meshes, eliminates invalid regions in a cut-and-fill strategy, and improves the minimal angle. We compared our method with four different state-of-the-art methods and found that our method showed a significant improvement over state-of-the-art methods in minimal angle, aspect ratio, and other meshing quality measurements. In addition, our method showed satisfactory results in terms of the ratio of regular vertices and the preservation of area and volume. [ABSTRACT FROM AUTHOR]

Personalized orthopedic casts largely improve the comfortability and functionality for patients compared to classical plaster casts. Current attempts on the geometric modeling of customized casts have not taken the swelling of the patients' broken extremities into account that causes a gradual deformation during the healing process. Thus, the shape of the injured body parts is not constant during wearing a cast, but may change along with a decreasing swelling. In this paper, we propose a framework for generating 3D printed personal casts that consistently fit and adapt to such deformations and allow to use a single cast throughout the whole healing process. The cast consists of two layers, an outer rigid layer, which provides enough strength and protection of the body parts and an underlying flexible layer that offers adaptability to the deformations. Both layers can be fabricated with standard 3D printers and easily be assembled around the injured body parts. We show a number of 3D printed results and demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Given a thin-sheet surface S in 3D, as well as a collection of sites located on S , it is a fundamental operation in digital geometry processing to partition S into a set of constituent surface patches in terms of proximity. Geodesic Voronoi diagram (GVD) provides a theoretically sound solution to this problem, but has limited use due to its high computational cost. Restricted Voronoi Diagram (RVD) considers the partitioning of S based on the straight-line distance. Under general conditions, RVD can yield a practicable solution with much less running time. However, when the input model contains thin sheets but the sites are sparse, RVD may present some issues due to the significant difference between the straight-line distance and the geodesic distance. The resulting flawed RVD cells limit the use of RVDs on further geometry processing occasions. In this paper, we address two kinds of commonly encountered issues: (1) a site dominates multiple disconnected regions, and (2) a site dominates one single region but the region is kidney-shaped. Both of the issues occur frequently, on a thin-sheet model with a relatively small number of sites. The first kind of flawed RVD cells occurs when the dominance of a site penetrates the other side of the surface. For such flawed RVD cells, it is easy to separate the remote parts from the principal region, based on connectivity. The second kind of flawed RVD cells occurs when a site is nearby a geometry edge and its dominating area crosses the geometry edge, leading to a kidney-shaped region. We observe that for a roughly flat RVD cell, the sum of its turning angles is close to 2 π. For a kidney-shaped region, however, the total angle becomes much larger than 2 π , which inspires us to develop an angle-based checking rule. We further propose a concept of "virtual site" to cut the region at the narrowest part and re-partition the ownerless region by the surrounding sites (may be virtual). All of the above-mentioned operations can be parallelized and generally only require lightweight computational relative to the RVD. After post-processing, the improved RVD has inconspicuous difference from the GVD, and is of much higher quality than LRVD (Yan et al., 2014) and EDBVD (Xin et al., 2022). We present its usage in intrinsic Delaunay triangulation (IDT) and centroidal Voronoi tessellation (CVT) based meshing. • Propose a parallel post-processing algorithm to fix the issues of the RVD. • We classify flawed RVD cells into two types T1 and T2. • For T1, isolate one or more ownerless regions from the principal region. • For T2, cut the kidney-shaped region into two parts and introduce a virtual site. • We further give a set of strategies for re-partitioning ownerless regions locally. [ABSTRACT FROM AUTHOR]

Generating an interior support structure is a key issue in 3D model geometric optimization for 3D printing. Most existing interior support structures have been designed by simulating lightweight structures naturally exist. One limitation of the existing method is that only one single structure is used for the model. However, different parts of a 3D model have different shapes and mechanical properties and different structures demonstrate distinctive advantages for supporting the model. Based on such observation, we propose to use hybrid structures for designing an optimal support structure. In this paper, we present a novel scheme of generating hybrid interior support structures for 3D printing. The proposed approach first partitions an input model into parts with different physical behaviors. Different interior structures are generated for each part, and the interior structures are finally joined together. Experimental results demonstrate that the proposed hybrid structure obtains higher strength-to-weight ratio than recent competing approaches that use single types of interior structures. [ABSTRACT FROM AUTHOR]

The point pair feature (PPF) is widely used in manufacturing for estimating 6-D poses. The key to the success of PPF matching is to establish correct 3-D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. However, efficient sampling of point pairs has been overlooked in existing frameworks. In this article, we propose a revised PPF matching pipeline to improve the efficiency of 6-D pose estimation. Our basic idea is that the valid scene reference points are lying on the object’s surface and the previously sampled reference points can provide prior information for locating new reference points. The novelty of our approach is a new sampling algorithm for selecting scene reference points based on the multisubpopulation particle swarm optimization guided by a probability map. We also introduce an effective pose clustering and hypotheses verification method to obtain the optimal pose. Moreover, we optimize the progressive sampling for multiframe point clouds to improve processing efficiency. The experimental results show that our method outperforms previous methods by 6.6%, 3.9% in terms of accuracy on the public DTU and LineMOD datasets, respectively. We further validate our approach by applying it in a real robot grasping task. [ABSTRACT FROM AUTHOR]

Low illumination, under which discriminative clues are buried in the captured images, is an under-investigated but noteworthy issue in wild scene text detection. Existing deep learning approaches suffer from the scarcity of training data and illumination-sensitive feature representation. To address these issues, we propose a Low Illumination Scene Text (LIST) Detector training with authentic synthetic data and integrating dedicated feature enhancement modules. Specifically, we adopt a lightweight and non-reference low-light scene text image synthesis network to acquire adequate training data through pixel-wisely adjusting the dynamic range curve of normal-light images. Moreover, illumination invariant feature representation is learned through dual path feature extraction stem with intensity adjusted inputs, and feature fusion branch with automatically designed fusion cell. In the end, the enhanced feature is fed into a segmentation-based layer to localize arbitrary shape text instances. We construct a labeled real-world scene text image dataset called "DarkText" and conduct extensive experiments to validate the advantages of our proposed framework over state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Fast and efficient fitting of accurate ellipses from data points has many applications in pattern recognition, machine vision, and robotics. However, the fitting accuracy may significantly degrade in the existence of outliers, such as the least-squares-based approaches. Despite robust methods attaining more accurate results than the least-squares manner under the contamination of outliers, they typically require the careful tuning of the hyper-parameters for good results. To mitigate the outlier disturbance, in this paper, we propose a conceptually simple yet quite useful preprocessing framework for high-precision ellipse fitting. Firstly, we leverage multi-scale operators to shrink the input image, by which a large number of outliers can be removed, followed by the smoothing of the sub-image to further improve the data quality. Then, we propose a key-point searching method to enhance the fitting precision via the analysis of the discrete pixel data in images. We prove that key-point-based ellipse fitting gives the upper bound of the approximation error generated by other sampled points with the same ellipse. Based on the key-point pairs inside and outside the ellipse, we further calculate their barycentric points and then perform fitting on these points to attain high-precision ellipses. We conduct extensive experiments on synthetic and real-world images to validate the proposed method and compare it with representative state-of-the-art approaches. Quantitative and qualitative results demonstrate that our method has more accurate and robust performance than competitors. Additionally, we employ the proposed method to compared approaches as a preprocessing step. Experiments demonstrate that our method is effective to significantly improve their fitting accuracy. Our source code is freely available at https://github.com/ChengQian09/MSKPF. • A simple yet quite useful method to improve the ellipse fitting performance. • A multi-scale smoothing module to remove the outlier disturbance. • A key-point searching technique to enhance the fitting accuracy. • Extensive experiments are implemented to demonstrate our method's advantages. [ABSTRACT FROM AUTHOR]

Computer-Aided Design (CAD) software remains a pivotal tool in modern engineering and manufacturing, driving the design of a diverse range of products. In this work, we introduce VQ-CAD, the first CAD generation model based on Denoising Diffusion Probabilistic Models. This model utilizes a vector quantized diffusion model, employing multiple hierarchical codebooks generated through VQ-VAE. This integration not only offers a novel perspective on CAD model generation but also achieves state-of-the-art performance in 3D CAD model creation in a fully automatic fashion. Our model is able to recognize and incorporate implicit design constraints by simply forgoing traditional data augmentation. Furthermore, by melding our approach with CLIP, we significantly simplify the existing design process, directly generate CAD command sequences from initial design concepts represented by text or sketches , capture design intentions, and ensure designs adhere to implicit constraints. • We proposed a novel and effective approach for CAD model generation by utilizing VQ-Diffusion. • Our model shows a unique ability to discern implicit design constraints. • We can transform image sketches and text descriptions into CAD sequences by leveraging the capabilities of CLIP. [ABSTRACT FROM AUTHOR]

Reverse engineering Computer-Aided Design (CAD) models based on the original geometry is a valuable and challenging research problem that has numerous applications across various tasks. However, previous approaches have often relied on excessive manual interaction, leading to limitations in reconstruction speed. To mitigate this issue, in this study, we approach the reconstruction of a CAD model by sequentially constructing geometric primitives (such as vertices, edges, loops, and faces) and performing Boolean operations on the generated CAD modules. We address the complex reconstruction problem in four main steps. Firstly, we use a plane to cut the input mesh model and attain a loop cutting line, ensuring accurate normals. Secondly, the cutting line is automatically fitted to edges using primitive information and connected to form a primitive loop. This eliminates the need for time-consuming manual selection of each endpoint and significantly accelerates the reconstruction process. Subsequently, we construct the loop of primitives as a chunked CAD model through a series of CAD procedural operations, including extruding, lofting, revolving, and sweeping. Our approach incorporates an automatic height detection mechanism to minimize errors that may arise from manual designation of the extrusion height. Finally, by merging Boolean operations, these CAD models are assembled together to closely approximate the target geometry. We conduct a comprehensive evaluation of our algorithm using a diverse range of CAD models from both the Thingi10K dataset and real-world scans. The results validate that our method consistently delivers accurate, efficient, and robust reconstruction outcomes while minimizing the need for manual interactions. Furthermore, our approach demonstrates superior performance compared to competing methods, especially when applied to intricate geometries. • We develop a systematic CAD reconstruction pipeline for reverse engineering CAD modeling, which ensures faithful CAD reconstruction with high precision and significantly reduces the reliance on manual or interactive operations. • We introduce an algorithm for automatically determining the height of the extruding body, effectively reducing errors that may occur during its construction. • We develop an efficient algorithm that rapidly fits the cutting line of a model to a primitive loop. This approach significantly reduces the time required compared to manual construction methods. [ABSTRACT FROM AUTHOR]

This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, i.e., point response intensity for each point, by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics-based approaches that are sensitive to outliers, we accommodate the defined point response intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors. Our implementation is publicly available at https://github.com/zikai1/GraphReg. [ABSTRACT FROM AUTHOR]

Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e., larger than the image resolution. The receptive field refers to the set of input pixels that are path-connected to a neuron. For image inpainting task, however, the size of surrounding areas needed to repair different kinds of missing regions are different, and the very large receptive field is not always optimal, especially for the local structures and textures. In addition, a large receptive field tends to involve more undesired completion results, which will disturb the inpainting process. Based on these insights, we rethink the process of image inpainting from a different perspective of receptive field, and propose a novel three-stage inpainting framework with local and global refinement. Specifically, we first utilize an encoder-decoder network with skip connection to achieve coarse initial results. Then, we introduce a shallow deep model with small receptive field to conduct the local refinement, which can also weaken the influence of distant undesired completion results. Finally, we propose an attention-based encoder-decoder network with large receptive field to conduct the global refinement. Experimental results demonstrate that our method outperforms the state of the arts on three popular publicly available datasets for image inpainting. Our local and global refinement network can be directly inserted into the end of any existing networks to further improve their inpainting performance. Code is available at https://github.com/weizequan/LGNet.git. [ABSTRACT FROM AUTHOR]

We present a system for automatically synthesizing a diverse set of semantically valid, and well-arranged 3D interior scenes for a given empty room shape. Unlike existing work on layout synthesis, that typically knows potentially needed 3D models and optimizes their location through cost functions, our technique performs the retrieval and placement of 3D models by discovering the relationships between the room space and the models׳ categories. This is enabled by a new analytical structure, called Wall Grid Structure , which jointly considers the categories and locations of 3D models. Our technique greatly reduces the amount of user intervention and provides users with suggestions and inspirations. We demonstrate the applicability of our approach on three types of scenarios: conference rooms, living rooms and bedrooms. [ABSTRACT FROM AUTHOR]

This paper presents a new method for detecting ellipses in images, which has many applications in pattern recognition and robotic tasks. Previous approaches typically use sophisticated arc grouping strategies or calculate differential such as tangents, and thereby they are less efficient or more sensitive to noise. In this work, we present a novel ellipse detector, based on the simple yet effective chord computation , and on the projective invariant cross ratio , which achieves promising performance in both accuracy and efficiency. First, elliptical arcs are extracted by fast vector computations along with the removal of straight segments to speed up detection. Then, arcs from the same ellipse are grouped together according to the relative location and the intersecting chord constraints , both are on coherent chord computation without differential. Additionally, an efficient additive principle is applied to further accelerate the grouping process. Finally, a novel and robust verification by area-deduced cross ratio is introduced to pick out salient ellipses. Compared with predecessor methods, cross ratio is not only simple for computation, but also has invariant properties (used to discriminate ellipses). Extensive experiments on seven public datasets (including synthetic and real-world images) are implemented. The results highlight the salient advantages of the proposed method compared to state-of-the-art detectors: Easier to implementation, more robust against occlusion and noise, as well as attaining higher F-measure. • A novel ellipse detection method that is accurate and capable of processing complex images. • A new and robust arc grouping strategy using chord computation without differential. • An effective verification scheme by the projective geometric invariant cross ratio. • A new industrial dataset with sufficient examination for ellipse detection methods. • Our method achieves the state-of-the-art results in both F-measure and robustness. [ABSTRACT FROM AUTHOR]

We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Fitting ellipses from unrecognized data is a fundamental problem in computer vision and pattern recognition. Classic least-squares based methods are sensitive to outliers. To address this problem, in this paper, we present a novel and effective method called hierarchical Gaussian mixture models (HGMM) for ellipse fitting in noisy, outliers-contained, and occluded settings on the basis of Gaussian mixture models (GMM). This method is crafted into two layers to significantly improve its fitting accuracy and robustness for data containing outliers/noise and has been proven to effectively narrow down the iterative interval of the kernel bandwidth, thereby speeding up ellipse fitting. Extensive experiments are conducted on synthetic data including substantial outliers (up to 60%) and strong noise (up to 200%) as well as on real images including complex benchmark images with heavy occlusion and images from versatile applications. We compare our results with those of representative state-of-the-art methods and demonstrate that our proposed method has several salient advantages, such as its high robustness against outliers and noise, high fitting accuracy, and improved performance. [ABSTRACT FROM AUTHOR]

Abstract: Aim of the study: To ascertain the effective constituents in Xiexin Decoction for anti-inflammation and the interactions of these constituents at the pharmacodynamic level. Materials and Methods: Rats were administered oral Xiexin Decoction 1h before intraperitoneal lipopolysaccharide. Nitric oxide production and Xiexin Decoction constituents in venous serum samples were quantified and the correlation between nitric oxide production and each constituent in serum was calculated. Raw264.7 cells were stimulated with lipopolysaccharide and one or more Xiexin Decoction constituents; cell viability and nitric oxide production was quantified. Results: Xiexin Decoction significantly decreased nitric oxide production in vivo, which correlated well with rhein, baicalin, emodin and aloe-emodin. All the typical constituents of Xiexin Decoction, with the exception of physcione and chrysophanol, dose-dependently inhibited nitric oxide production in vitro. In an orthogonal designed in vitro study, rhein was the most powerful constituent, followed by baicalin then berberine and no synergy was found among these constituents. Conclusions: Rhein was the most effective anti-inflammatory constituent in Xiexin Decoction followed by baicalin; no synergy was observed between rhein, baicalin and berberine at the pharmacodynamic level in vitro. [Copyright &y& Elsevier]

• A novel circle detection method based on inscribed triangles, which is resistant to occlusion and robust to noise. • A new arc grouping strategy using the relative position constraint and the inscibed triangle constraint.. • A total geometry-based parameter estimation method by inscribed triangles without the dependence of least-square fitting but with the equivalent accuracy. • A new collected real-world dataset with the sufficient examination for circle detection methods. • Extensive experiments compared with representative state-of-the-art methods and better results have been obtained both in F-measure and execution speed. Circle detection is a critical issue in pattern recognition and image analysis. Conventional geometry-based methods such as tangent or symmetry are sensitive to noise or occlusion. Area computation is more robust against noise, because it avoids differential calculations. Inspired by this characteristic, we present a novel method for fast circle detection using inscribed triangles. The proposed algorithm, which is robust to noise and resistant to occlusion, first extracts circular arcs by approximating line segments and identifying inflection points and sharp corners. To speed up the computation, irrelevant segments are filtered out through the triangle inequality. Arcs that belong to the same circle are then combined according to the position constraint and the inscribed triangle constraint. The circle parameters are further estimated by inscribed triangles based upon the Theil-Sen estimator and linear error refinement without the dependence of least-square fitting but still with the equivalent accuracy. Finally, candidate circles are verified to prune false positives through an inlier ratio rule, which jointly considers both distance and angle deviations. Extensive experiments are conducted on synthetic images including overlapping circles, and real images from four diverse datasets (three publicly available and one we built). Results are compared with those of representative state-of-the-art methods, and the proposed method is demonstrated to embraces several advantages: resistant to occlusion, more robust to noise, and better performance and efficiency. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a fully automatic framework that tessellates industrial computer-aided design (CAD) models into high-quality triangular meshes. In contrast to previous approaches that are purely parametric or performed directly in 3D space, our method is based on a remeshing algorithm that can achieve accuracy and high-quality simultaneously. Given an input standard CAD model, which is represented by B-rep format,we first rebuild the parametric domain for each surface patch according to an initial triangulation, and then the boundaries of the parametric domain are retriangulated by exploiting the Constrained Delaunay Triangulation (CDT). In the second stage, the 2D triangulation is projected back to the 3D space, and a modified global isotropic remeshing process is applied, which further improves the regularity and angle quality of the tessellated meshes. Experiments demonstrate the validity of the proposed approach and its ability to generate high-quality meshes. Moreover, we evaluate our technique and compare it with state-of-the-art CAD model tessellation approaches. Highlights • An automatic surface tessellation framework for complex industrial CAD models. • Improvement of previous remeshing approach to generate high-quality surface meshes. • Introduction of simple yet effective heuristics to merge the small patches. [ABSTRACT FROM AUTHOR]

Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. • We introduce reference-based SR in down/up-sampling-based video coding method, where target and reference images are not required to be texture-aligned as required in existing methods. • We proposed an adaptive group of pictures (GOP) method to automatically decide the adaptive sampling scheme. • The neural texture transfer model for reference-based SR produces realistic up-sampled frame at the decoding end. [ABSTRACT FROM AUTHOR]

We present a novel method for high-quality blue-noise sampling on mesh surfaces with prescribed cell-sizes for the underlying tessellation (capacity constraint). Unlike the previous surface sampling approach that only uses capacity constraints as a regularizer of the Centroidal Voronoi Tessellation (CVT) energy, our approach enforces an exact capacity constraint using the restricted power tessellation on surfaces. Our approach is a generalization of the previous 2D blue noise sampling technique using an interleaving optimization framework. We further extend this framework to handle multi-capacity constraints. We compare our approach with several state-of-the-art methods and demonstrate that our results are superior to previous work in terms of preserving the capacity constraints. [ABSTRACT FROM AUTHOR]

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies. [ABSTRACT FROM AUTHOR]

We introduce a new method to interpolate scattered 3D data points with a single B-spline surface patch which is globally smooth. Given a set of scattered 3D data points and their corresponding parametrization, our method first constructs a set α of B-spline bases using a weighted strategy, and inserts knots to the knot vectors based on α. Then, the knot insertion procedure is iterated until a set β of B-spline bases exists, which indicates the existence of the interpolation surface. Finally, by applying the fairing energy minimizing with interpolation constraints, a globally smooth B-spline surface which interpolates the data points can be produced. Experimental results demonstrate that the generated B-spline surfaces often have fewer control points than those of traditional methods, while keeping the scattered data points interpolated accurately. The implementation of our algorithm and the scripts to reproduce all the results are available at https://github.com/wangbolun300/sparse_data. • An iterative knot insertion algorithm is introduced to guarantee the existence of B-spline interpolation surfaces when fitting scattered 3D data points. • The generated B-spline surfaces often use fewer control points than those of tranditional interpolation methods. • A novel parameter δ is introduced to improve the stability of control point solving. • The control points can be solved by a global fairing energy minimization procedure with strict interpolation constraints. Thus, the generated B-spline surface is globally smooth. [ABSTRACT FROM AUTHOR]

A protein with high purity has become an essential pre-requisite for investigating its bioactivity, molecular structure and characteristics. Therefore, the development of technologies for efficient purification of protein is urgently necessary. The objective of this study was to establish a purification protocol for a recombinant protein rG17PE38. Different forms of chromatography such as hydrophobic interaction and ion exchange chromatography were chosen as the core purification steps. The performance of each technique was optimized to meet the requirements and the purification steps were arranged in a logical way of facilitating to operate in next step. In addition, some characteristics of the protein such as stability, bioactivity and cellular location were determined. Finally, whether the protein could induce cell apoptosis was also explored. The results showed the protein purified via the suggested three-step purification scheme could obtain a purity of 95%, and its bioactivity in the form of IC 50 was 17.6 ng/mL, furthermore it could keep stable at 4 °C for at least 10 days. The protein could bind on its target cell membrane specifically, and inducing cell apoptosis was demonstrated to be one of the cytotoxicity mechanisms of the protein. Results obtained in our study may provide useful information on strategies of protein purification and lay a substantial foundation for the followed animal or clinical experiments on rG17PE38. [ABSTRACT FROM AUTHOR]

Abstract: Egg allergy is an important public health and safety concern, so quantification and administration of food or vaccines containing ovalbumin (OVA) are urgently needed. This study aimed to establish a rapid and sensitive magnetic particles–chemiluminescence enzyme immunoassay (MPs–CLEIA) for the determination of OVA. The proposed method was developed on the basis of a double antibodies sandwich immunoreaction and luminol–H2O2 chemiluminescence system. The MPs served as both the solid phase and separator, the anti-OVA MPs-coated polyclonal antibodies (pAbs) were used as capturing antibody, and the horseradish peroxidase (HRP)-labeled monoclonal antibody (mAb) was taken as detecting antibody. The parameters of the method were evaluated and optimized. The established MPs–CLEIA method had a linear range from 0.31 to 100ng/ml with a detection limit of 0.24ng/ml. The assays showed low reactivities and less than 5% of intraassay and interassay coefficients of variation (CVs), and the average recoveries were between 92 and 97%. Furthermore, the developed method was applied in real samples analysis successfully, and the correlation coefficient with the commercially available OVA kit was 0.9976. Moreover, it was more rapid and sensitive compared with the other methods for testing OVA. [Copyright &y& Elsevier]

• A realistic and diversified training dataset (POL) is constructed by optical modeling for reflection obstructed image. • A progressive polarization based reflection removal network (P 2 R 2 Net) with up-to-date neural network units is proposed. • The improvement of the proposed method for real-world images is verified by comparisons with the state-of-the-art methods. • Robustness analysis on reflection-tainted synthesis images suggests our method possesses excellent generalization ability. The reflection effect is unavoidable when taking photos through glasses or other transparent materials, which introduces undesired information into pictures. Hence, removing the influence of reflection becomes a key problem in computer vision. One of the main obstacles of recent learning based approaches is the lacking of realistic training data. To address this issue, we introduce a new dataset synthesis method as well as a novel neural network architecture for single image reflection removal. First, we make use of the polarization characteristics of light into the synthesis of datasets, so as to obtain more realistic and diversified training dataset POL. Then, we design a novel Progressive Polarization based Reflection Removal Network (P 2 R 2 Net), which preliminary estimates the coarse background layer to guide the final reflection removal. We demonstrate that our method performs better than the state-of-the-art single image reflection removal methods through quantitative and qualitative experimental comparisons. Specifically, the average PSNR of our restored images selected from three representative benchmark datesets: "Real20", " SI R 2 " and "Nature" is improved at least 0.49 compared with existing methods and reaches to 24.52. [ABSTRACT FROM AUTHOR]

We propose a geometry-guided neural network architecture for robust and detail-preserving surface normal estimation for unstructured point clouds. Previous deep normal estimators usually estimate the normal directly from the neighbors of a query point, which lead to poor performance. The proposed network is composed of a weight learning sub-network (WL-Net) and a lightweight normal learning sub-network (NL-Net). WL-Net first predicates point-wise weights for generating an optimized point set (OPS) from the input. Then, NL-Net estimates a more accurate normal from the OPS especially when the local geometry is complex. To boost the weight learning ability of the WL-Net, we introduce two geometric guidance in the network. First, we design a weight guidance using the deviations between the neighbor points and the ground truth tangent plane of the query point. This deviation guidance offers a "ground truth" for weights corresponding to some reliable inliers and outliers determined by the tangent plane. Second, we integrate the normals of multiple scales into the input. Its performance and robustness are further improved without relying on multi-branch networks, which are employed in previous multi-scale normal estimators. Thus our method is more efficient. Qualitative and quantitative evaluations demonstrate the advantages of our approach over the state-of-the-art methods, in terms of estimation accuracy, model size and inference time. Code is available at https://github.com/2429581027/local-geometric-guided. • A new two-step normal estimation method. • Integrate geometric priors into deep learning framework. • Replace multi-scale architecture by multi-scale geometric input. • Achieve 10.79 angle error in comparison with the previous state of the art of 11.78. [ABSTRACT FROM AUTHOR]

Abstract: Anisotropic triangle meshes are used for efficient approximation of surfaces and flow data in finite element analysis, and in these applications it is desirable to have as few obtuse triangles as possible to reduce the discretization error. We present a variational approach to suppressing obtuse triangles in anisotropic meshes. Specifically, we introduce a hexagonal Minkowski metric, which is sensitive to triangle orientation, to give a new formulation of the centroidal Voronoi tessellation (CVT) method. Furthermore, we prove several relevant properties of the CVT method with the newly introduced metric. Experiments show that our algorithm produces anisotropic meshes with much fewer obtuse triangles than using existing methods while maintaining mesh anisotropy. [Copyright &y& Elsevier]

Abstract: In order to detect nodularin (NOD) from water and fish samples, a monoclonal antibody (mAb) specific for NOD was selected which has no cross-reactivity with other toxins (<0.4%). Based on the mAb, a competitive enzyme-linked immunosorbent assay (ELISA) was developed. The working range of the ELISA was from 0.5 to 100 ng mL−1 with a detection limits of 0.16 ng mL−1. The average recoveries of NOD from sea water, flounder and blue mussels were 87.8%, 90.7% and 86.9%, respectively with the coefficients of variation (CVs) from 2.1% to 3.2%. Therefore, the developed competitive ELISA may become a new convenient and satisfied analytical tool for detecting of NOD in water and fish samples. [Copyright &y& Elsevier]

Abstract: A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy–Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction. [Copyright &y& Elsevier]

Meaningful feature curves provide high-level shape representation of the geometrical shapes and are useful in various applications. In this paper, we propose an automatic method on the basis of the quadric surface fitting technique to extract complete feature curve networks (FCNs) from 3D surface meshes, as well as finding cycles and generating a high-quality segmentation. In the initial collection of noisy and fragmented feature curves, we first fit the quadric surfaces of each curve and the corresponding neighbor vertices to filter out non-salient or noisy feature curves. Then we conduct a feature extension step to address the curve intersections and form a closed FCN. Finally, we regard circle curves as cycles in the complete FCN and segment the mesh into patches to reveal a highly structured representation of the input geometry. Experimental results demonstrate that our algorithm is more robust for FCN extraction from complex input meshes and achieves higher quality patch layouts compared with the state-of-the-art approaches. We also verify the validity of extracted feature curve cycles by applying them to surface reconstruction. • We provide an efficient method to preserve meaningful feature curves and extract complete FCNs from 3D models. • We simultaneously find the curve cycles in the network and generate high-quality patch layouts. • We obtain a compact high-level representation of 3D models that can be used for surface reconstruction. [ABSTRACT FROM AUTHOR]