The largest arthropod in Earth history: insights from newly discovered Arthropleura remains (Serpukhovian Stainmore Formation, Northumberland, England)

Arthropleura is a genus of giant myriapods that ranged from the early Carboniferous to Early Permian, with some individuals attaining lengths >2 m. Although most of the known fossils of the genus are disarticulated and occur primarily in late Carboniferous (Pennsylvanian) strata, we report here partially articulated Arthropleura remains from the early Carboniferous Stainmore Formation (Serpukhovian; Pendleian) in the Northumberland Basin of northern England. This 76 × 36 cm specimen represents part of an exuvium and is notable because only two comparably articulated giant Arthropleura fossils are previously known. It represents one of the largest known arthropod fossils and the largest arthropleurid recovered to date, the earliest (Mississippian) body fossil evidence for gigantism in Arthropleura, and the first instance of a giant arthropleurid body fossil within the same regional sedimentary succession as the large arthropod trackway Diplichnites cuithensis. The remains represent 12–14 anterior Arthropleura tergites in the form of a partially sand-filled dorsal exoskeleton. The original organism is estimated to have been 55 cm in width and up to 2.63 m in length, weighing c. 50 kg. The specimen is preserved partially in three dimensions within fine sandstone and has been moderately deformed by synsedimentary tectonics. Despite imperfect preservation, the specimen corroborates the hypothesis that Arthropleura had a tough, sclerotized exoskeleton. Sedimentological evidence for a lower delta plain depositional environment supports the contention that Arthropleura preferentially occupied open woody habitats, rather than swampy environments, and that it shared such habitats with tetrapods. When viewed in the context of all the other global evidence for Arthropleura, the specimen contributes to a dataset that shows the genus had an equatorially restricted palaeogeographical range, achieved gigantism prior to late Paleozoic peaks in atmospheric oxygen, and was relatively unaffected by climatic events in the late Carboniferous, prior to its extinction in the early Permian.Supplementary material: Images of 3D mesh model of Arthropleura are available at https://doi.org/10.6084/m9.figshare.c.5715450

An efficient data masking method for encrypted 3D mesh model

The industrial 3D mesh model (3DMM) plays a significant part in engineering and computer aided designing field. Thus, protecting copyright of 3DMM is one of the major research problems that require significant attention. Further, the industries started outsourcing its 3DMM to cloud computing (CC) environment. For preserving privacy, the 3DMM are encrypted and stored on cloud computing environment. Thus, building efficient data masking of encrypted 3DMM is considered to be efficient solution for masking information of 3DMM. First, using the secret key, the original 3DMM is encrypted. Second without procuring any prior information of original 3DMM it is conceivable mask information on encrypted 3D mesh models. Third, the original 3DMM are reconstructed by extracting masked information. The existing masking methods are not efficient in providing high information masking capacity in reversible manner and are not robust. For overcoming research issues, this work models an efficient data masking (EDM) method that is reversible nature. Experiment outcome shows the EDM for 3DMM attain better performance in terms of peak signal-to-noise ratio (PSNR) and root mean squared error (RMSE) over existing data masking methods. Thus, the EDM model brings good tradeoffs between achieving high data masking capacity with good reconstruction quality of 3DMM.

A simplified algorithm for 3D mesh model considering the influence of edge features

Aiming at the problem that most of the existing grid simplification algorithms for 3D models can not deal with a large number of boundary or non-popular grid models, this paper proposes a grid simplification algorithm for 3D models based on traditional algorithms. The algorithm mainly studies the geometric features of the model, considering the calculation methods and characteristics of edge shrinkage, and introduces the edge feature factors on the basis of the traditional algorithm, that is, the triangular area and side length factors of local area are introduced in the calculation of folding cost. In addition, the gaussian curvature characteristics of the 3D model are also included. Experimental results show that the proposed algorithm can keep the detail features of the mesh model well, and greatly reflect the quality and effect of mesh simplification after simplification.

Feature-Point Matching for Aerial and Ground Images by Exploiting Line Segment-Based Local-Region Constraints

In this study, we propose a feature-point matching method that is robust to viewpoint, scale, and illumination changes between aerial and ground images, to improve matching performance. First, a 3D rendering strategy is adopted to synthesize ground-view images from the 3D mesh model reconstructed from aerial images and overcome the global geometric distortion between aerial and ground images. We do not directly match feature points between the synthesized and ground images, but extract line-segment correspondences by designing a line-segment matching method that can adapt to the local geometric deformation, holes, and blurred textures on the synthesized image. Then, on the basis of the line-segment matches, local-region correspondences are constructed, and local regions on the synthesized image are propagated back to the original aerial images. Lastly, feature-point matching is performed between the aerial and ground images with the constraints of the local-region correspondences. Experimental results demonstrate that the proposed method can obtain more correct matches and higher matching precision than state-of-the-art methods. Specifically, the proposed method increases the average number of correct matches and average matching precision of the second-best method by more than five times and 40%, respectively.

Geometric simulation for warp-knitted tubular bandages with the mesh model

The purpose of this paper is to geometrically simulate warp-knitted medical tubular bandages with a computer-aided simulator. A flat mesh model is established according to unfolded fabric considering the knitting characteristics of double-needle bed warp-knitted tubular fabrics. Moreover, a 3D (three-dimensional) mesh model corresponding to the actual product shape is created. To better describe the spatial geometry of stitches, eight-point models are introduced, and stitches are generated with the flat mesh model. Founded on matrix operations, the stitch position in the 3D mesh model is determined through coordinate mapping. Various stitch paths are rendered in computer programming languages C# and JavaScript to conduct simulations. Warp-knitted medical tubular bandages with a large number of shapes are effectively modeled.

Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

3D Mesh Model Classification with a Capsule Network

With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimensional representation as a complex data structure provides an effective shape approximate representation for the three-dimensional object. Although the traditional method can extract the characteristics of the three-dimensional object through the graphical method, it cannot be applied to more complex objects. However, due to the complexity and irregularity of the mesh data, it is difficult to directly apply convolutional neural networks to 3D mesh data processing. Considering this problem, we propose a deep learning method based on a capsule network to effectively classify mesh data. We first design a polynomial convolution template. Through a sliding operation similar to a two-dimensional image convolution window, we directly sample on the grid surface, and use the window sampling surface as the minimum unit of calculation. Because a high-order polynomial can effectively represent a surface, we fit the approximate shape of the surface through the polynomial, use the polynomial parameter as the shape feature of the surface, and add the center point coordinates and normal vector of the surface as the pose feature of the surface. The feature is used as the feature vector of the surface. At the same time, to solve the problem of the introduction of a large number of pooling layers in traditional convolutional neural networks, the capsule network is introduced. For the problem of nonuniform size of the input grid model, the capsule network attitude parameter learning method is improved by sharing the weight of the attitude matrix. The amount of model parameters is reduced, and the training efficiency of the 3D mesh model is further improved. The experiment is compared with the traditional method and the latest two methods on the SHREC15 data set. Compared with the MeshNet and MeshCNN, the average recognition accuracy in the original test set is improved by 3.4% and 2.1%, and the average after fusion of features the accuracy reaches 93.8%. At the same time, under the premise of short training time, this method can also achieve considerable recognition results through experimental verification. The three-dimensional mesh classification method proposed in this paper combines the advantages of graphics and deep learning methods, and effectively improves the classification effect of 3D mesh model.