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Point cloud registration is not only a key step in constructing high-precision 3-D maps, but is also of great significance in fields such as autonomous navigation, environmental perception, and robot operation. However, in outdoor environments, point cloud registration tasks face challenges such as complex terrains and object occlusion, leading to significant overlap and incomplete data. To address these challenges, this article proposes a novel approach that utilizes geometric Transformer for precise point cloud registration. This method employs a learnable geometric position update (GPU) module that automatically learns and captures the geometric structure and features among point cloud data. Additionally, the algorithm introduces a deeper cross-attention (DCA) module that applies deep convolutional operations on the key and value of cross-attention, enabling the attention mechanism to capture both local spatial structures and global contextual information of the point cloud. Through information interaction, the expressiveness and adaptability of the point cloud registration model can be enhanced. Experimental evaluations on multiple benchmark tests, including 3DMatch/3DLoMatch, KITTI, ModelNet/ModelLoNet, and MVP-RG, demonstrate the competitive performance of our algorithm. Compared to the UDPReg and REGTR algorithms, our method demonstrates performance improvements in the RR metric, increasing by 11.3% and 10.8%, respectively, in the low-overlap case.

China is a multi‐ethnic country. It is of great significance for the skull identification to realize the skull ethnic classification through computers, which can promote the development of forensic anthropology and accelerate the exploration of national development. In this paper, the 3D skull model is transformed into 2D auxiliary image including curvature, depth and elevation information, and then the deep learning method of the 2D auxiliary image is used for ethnic classification. We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms. Experiments on 400 skull models have been conducted for ethnic classification by our method. We set different learning rates to compare the performance of the model, the highest accuracy of ethnic classification is 98.75%, which have better performance than other five classical neural network structures. Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance. Author summary:Skull ethnic classification is of great significance in anthropology, forensic science and archaeology. We put forward a method of ethnic classification which combines deep learning with 2D skull auxiliary image containing depth, curvature and elevation information. The skull features are automatically extracted and classified by our network. Experimental results show that our method can obtain excellent performance of ethnic classification. In the future, we will extend the method to multi ethnic classification.

This study proposes a novel, to the best of our knowledge, transformer-based end-to-end network (TDNet) for point cloud denoising based on encoder–decoder architecture. The encoder is based on the structure of a transformer in natural language processing (NLP). Even though points and sentences are different types of data, the NLP transformer can be improved to be suitable for a point cloud because the point can be regarded as a word. The improved model facilitates point cloud feature extraction and transformation of the input point cloud into the underlying high-dimensional space, which can characterize the semantic relevance between points. Subsequently, the decoder learns the latent manifold of each sampled point from the high-dimensional features obtained by the encoder, finally achieving a clean point cloud. An adaptive sampling approach is introduced during denoising to select points closer to the clean point cloud to reconstruct the surface. This is based on the view that a 3D object is essentially a 2D manifold. Extensive experiments demonstrate that the proposed network is superior in terms of quantitative and qualitative results for synthetic data sets and real-world terracotta warrior fragments.

The emergence of the three-dimensional (3D) scanner has greatly benefited archeology, which can now store cultural heritage artifacts in computers and present them on the Internet. As many Terracotta Warriors have been predominantly found in fragments, the pre-processing of these fragments is very important. The raw point cloud of the fragments has lots of redundant points; it requires an excessively large storage space and much time for post-processing. Thus, an effective method for point cloud simplification is proposed for 3D Terracotta Warrior fragments. First, an algorithm for extracting feature points is proposed that is based on local structure. By constructing a k-dimension tree to establish the k-nearest neighborhood of the point cloud, and comparing the feature discriminant parameter and characteristic threshold, the feature points, as well as the non-feature points, are separated. Second, a deep neural network is constructed to simplify the non-feature points. Finally, the feature points and the simplified non-feature points are merged to form the complete simplified point cloud. Experiments with the public point cloud data and the real-world Terracotta Warrior fragments data are designed and conducted. Excellent simplification results were obtained, indicating that the geometric feature can be preserved very well.

Transferring a photo to a stylised image with beautiful texture has become one of the most popular topics in computer vision and the application of image processing. Controlling the stroke size of the texture is one of the challenging problems in this task. Recent representative methods for such problem introduce a pyramid model to regulate receptive fields in the network. Meanwhile, dilated convolutions are proved to be a very efficient way to adjust receptive fields without losing resolution. By combining the advantages of both approaches and making special optimisation for VGG19 model for style transfer tasks, the authors propose to exploit dilated convolutions to extract texture information endowing the network with stroke controllable. Several sets of contrast experiments were conducted and results show that their algorithm can generate more attractive stylisation images and control stroke size flexibly. It demonstrates the superiority of applying dilated convolutions as a texture extraction method for maintaining more texture information and controlling stroke size.

Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo. However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and in vivo experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT.

Rock-art has been scratched, carved, and pecked into rock panels all over the world resulting in a huge number of engraved figures on natural rock surfaces that record ancient human life and culture. To preserve and recognize these valuable artifacts of human history, 2D digitization of rock surfaces has become a suitable approach due to the development of powerful 2D image processing techniques in recent years. In this article, we present a novel systematical framework for the segmentation of different petroglyph figures from 2D high-resolution images. The novel boundary enhancement with Gaussian loss (BEGL) function is proposed aiming at refining and smoothing the rock-arts boundaries in the basic UNet architecture. Several experiments on the 3D-pitoti dataset demonstrate that our proposed approach can achieve more accurate boundaries and superior results compared with other loss functions. The comprehensive framework of petroglyph segmentation from 2D high-resolution images provides the foundation for recognizing multiple petroglyph marks. The framework can then be extended to other cultural heritage digital protection domain easily.

This paper presents a method for simulating surface crack patterns appearing in ceramic glaze, glass, wood and mud. It uses a physically and heuristically combined method to model this type of crack pattern. A stress field is defined heuristically over the triangle mesh of an object. Then, a first-order quasi-static cracking node method (CNM) is used to model deformation. A novel combined stress and energy combined crack criterion is employed to address crack initiation and propagation separately according to physics. Meanwhile, a highest-stress-first rule is applied in crack initiation, and a breadth-first rule is applied in crack propagation. Finally, a local stress relaxation step is employed to evolve the stress field and avoid shattering artifacts. Other related issues are also discussed, such as the elimination of quadrature sub-cells, the prevention of parallel cracks and spurious crack procession. Using this method, a variety of crack patterns observed in the real world can be reproduced by changing a set of parameters. Consequently, our method is robust because the computational mesh is independent of dynamic cracks and has no sliver elements. We evaluate the realism of our results by comparing them with photographs of real-world examples. Further, we demonstrate the controllability of our method by varying different parameters.

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

The existing 3D modeling studies of Chinese ancient architecture are mostly procedure driven and rely on fixed construction rules. Therefore, these methods have limited applications in virtual reality (VR) engineering. We propose a data-driven approach to synthesize 3D models from existing 3D data that provides more flexibility and fills the gap between academic studies and VR engineering. First, 3D architecture models were preprocessed and decomposed into components, and the components were clustered by their geometric features. Second, a Bayesian network was generated by learning from the dataset to represent the internal relationships between the architectural components. Third, the inference results of the trained network were utilized to generate a reasonable relationship matching to support the synthesis of the structural components. The proposed method can be used in 3D content creation for VR development and directly supports VR applications in practice.

Virtual digital resources and printed models have become indispensable tools for medical training and surgical planning. Nevertheless, printed models of soft tissue organs are still challenging to reproduce. This study adopts open source packages and a low-cost desktop 3D printer to convert multiple modalities of medical images to digital resources (volume rendering images and digital models) and lifelike printed models, which are useful to enhance our understanding of the geometric structure and complex spatial nature of anatomical organs. Neuroimaging technologies such as CT, CTA, MRI, and TOF-MRA collect serial medical images. The procedures for producing digital resources can be divided into volume rendering and medical image reconstruction. To verify the accuracy of reconstruction, this study presents qualitative and quantitative assessments. Subsequently, digital models are archived as stereolithography format files and imported to the bundled software of the 3D printer. The printed models are produced using polylactide filament materials. We have successfully converted multiple modalities of medical images to digital resources and printed models for both hard organs (cranial base and tooth) and soft tissue organs (brain, blood vessels of the brain, the heart chambers and vessel lumen, and pituitary tumor). Multiple digital resources and printed models were provided to illustrate the anatomical relationship between organs and complicated surrounding structures. Three-dimensional printing (3DP) is a powerful tool to produce lifelike and tangible models. We present an available and cost-effective method for producing both digital resources and printed models. The choice of modality in medical images and the processing approach is important when reproducing soft tissue organs models. The accuracy of the printed model is determined by the quality of organ models and 3DP. With the ongoing improvement of printing techniques and the variety of materials available, 3DP will become an indispensable tool in medical training and surgical planning.

Using data from the Cluster spacecraft from January 2003 to December 2004, we perform a statistical study on some properties of the field-aligned electron (FAE) events and interplanetary magnetic field (IMF) Bydependence of FAE events with different durations in high-altitude polar regions. A total of 1335 FAE events were observed by the C3 spacecraft. More down-flowing events were observed in the Southern Hemisphere, and more up-flowing events were observed in the Northern Hemisphere. It proves that down-flowing events mainly originate from magnetosphere or solar wind and up-flowing events are mainly derived from ionosphere. Short-lifetime events showed a morning concentration in the magnetic local time distribution, and long-lifetime events were concentrated both before and after noon. For the IMF Bydependence of the FAE events, short-lifetime events were much affected by IMF Byand resulted in a morning concentration, while the long-lifetime events were almost unaffected by IMF By. With further analysis, we determined that the short-lifetime and long-lifetime events had different sources.

Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.

AbstractNuclear DNA C-values are the basic data of species and used in a strikingly wide range of biological fields. Genome size of the most species belonging to the three families Nulembonaceae, Cabombaceae and Nym-phaeaceae in Nymphaeales were not assessed, especially none of the species in Cabombaceae was reported so far. In present research, flow cytometry was used to assess the genome size of 12 species belonging to the three families in Nymphaeales and a standard squash technique to count the chromosome numbers of the tested materials. In the tested different species, 2C nuclear DNA contents ranged from 1.55 to 8.11 pg, which is more than fivefold variation. And chromosome numbers varied from 2n=16 to 2n=72. Differences between the species or among populations within a species were also recorded by statistical analysis. The tested species are all small genomes except two species of Victoria.The ploidies did not matched with their DNA contents very well in Nymphaea.Significant differences were found between the species of Nymphaea, between those of Victoriaand among wild populations of Nelumbo nicifera., while no obvious differences were found between the species of Nelumboand among the cultivars of Euryale feroxas well as among those of Brasenia schreberi, respectively.

The kudzu root is one of the earliest medicinal plants listed in traditional Chinese medicine. In this paper, chemical compositions changes of kudzu roots from one year old to five years old were analyzed with respect to puerarin, acid-insoluble polysaccharides, acid-soluble polysaccharides, reducing sugar, protein, free amino acids, and lipid. In addition, the puerarin content was determined by high performance liquid chromatography (HPLC) method. The results showed that acid-soluble polysaccharides content of kudzu root increased with each growth period. In contrast, the acid-insoluble polysaccharides decreased significantly. The contents of reducing sugar and puerarin in kudzu root decreased significantly during its growth period. Beyond that, the contents of protein, free amino acids, and lipid in kudzu root ranged from 31.8 to 45.8 g/kg, 2.21 to 4.33 g/kg, and 32.2 to 76.9 g/kg, respectively. The trend of protein content coincided with the total content of free amino acids, in contrast to lipid. This paper provides a set of data and the select of kudzu root for the processing and development of new products of kudzu root.