CircNet: Meshing 3D Point Clouds with Circumcenter Detection

Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface. Taking advantage of learning-based techniques in triangulation, existing methods enumerate the complete combinations of candidate triangles, which is both complex and inefficient. In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation. Specifically, we introduce multiple anchor priors to divide the neighborhood space of each point. The neural network then learns to predict the presences and locations of circumcenters under the guidance of those anchors. We extract the triangles dual to the detected circumcenters to form a primitive mesh, from which an edge-manifold mesh is produced via simple post-processing. Unlike existing learning-based triangulation methods, the proposed method bypasses an exhaustive enumeration of triangle combinations and local surface parameterization. We validate the efficiency, generalization, and robustness of our method on prominent datasets of both watertight and open surfaces. The code and trained models are provided at https://github.com/Ruitao-L/CircNet.Comment: accepted to ICLR202

EasyNet: An Easy Network for 3D Industrial Anomaly Detection

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pre-trained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pre-trained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU

Effect of sulfur on enhancing nitrogen-doping and magnetic properties of carbon nanotubes

Sulfur (S) is introduced as an additive in the growth atmosphere of carbon nanotubes (CNTs) in the range of 940-1020°C. CNT products with distorted sidewalls can be obtained by S-assisted growth. Moreover, many fascinating CNT structures can also be found in samples grown with S addition, such as bamboo-like CNTs, twisted CNTs, arborization-like CNTs, and bead-like CNTs. Compared with CNTs grown without S, more nitrogen-doping content is achieved in CNTs with S addition, which is beneficial for the properties and applications of nitrogen-doped CNTs. In addition, S can also enhance the encapsulation of ferromagnetic materials and thus improve the soft magnetic properties of CNTs, which is favorable to the applications of CNTs in the electromagnetic wave-absorbing and magnetic data storage areas

Real3D-AD: A Dataset of Point Cloud Anomaly Detection

High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD

Design and analysis of miniaturized low profile and second-order multi-band polarization selective surface for multipath communication application

In this paper, a novel frequency selective surface (FSS) is designed; it has the characteristics of the low profile, second-order, multi-band, and the remarkable polarization selection properties. In the following, such an FSS having polarization selection characteristics will be referred to simply as a polarization selection surface (PSS). In a specific frequency band, the proposed PSS has a second-order selective transmission characteristic for TE and TM waves. Based on the coupling resonance filtering mechanism, the proposed PSS is composed of three metallic layers separated by two layers of dielectric substrates, which serves as the spatial form of the second-order microwave filter. The proposed PSS uses a sub-wavelength periodic structure array consisting of a non-resonant unit, and the unit size and the period within the range of 0.08λ 1 -0.15λ 1 , where the λ 1 =40.76 mm is the first passband wavelength of free space, so the PSS miniaturization characteristic is remarkable. The theoretical analysis and measure results show that the proposed bandpass PSS has good second-order polarization selection characteristics, out-of-band suppression level, and the flat transmission band, compared with the first-order bandpass PSS. In the range of incident angle of 0°-60°, it has a stable frequency response. It provides a reference for the design of a polarization wave generator and a polarization separation structure in a multipath communication system. © 2019 IEEE

A Study on Central Lymph Node Metastasis in 543 cN0 Papillary Thyroid Carcinoma Patients

Background. Papillary thyroid carcinoma (PTC) with central lymph node metastases (CLNMs) is common. The objective of this study was to investigate the incidence and risk factors of lymph node metastasis patients with PTC. Patients and Methods. Between January 2013 and February 2015, a retrospective study of 543 patients with PTC undergoing hemithyroidectomy or total thyroidectomy with routine central lymph node dissection (CLND) was analyzed. Clinicopathologic risk factors for CLNM were studied using univariate and multivariate analysis by SPSS 22.0 software. Results. The incidence of CLNMs in PTC patients was 38.1% (207/543). In the multivariate analysis, male gender ( < 0.001, OR: 1.984), age <45 years ( < 0.001, OR: 1.934), bilaterality ( = 0.006, OR: 1.585), tumor size ≥0.25 cm ( = 0.001, OR: 7.655), and external extension ( = 0.001, OR: 7.579) were independent risk factors of CLNMs. Furthermore, in PTC patients with tumor size <0.25 cm, all 7 males and 21 patients with unilaterality were not found to have CLNMs. Conclusions. CLNMs are prevalent in the PTC patients with the following risk factors: male gender, age <45 years, bilaterality, tumor size ≥0.25 cm, and external extension. PTC patients with tumor size <0.25 cm, male patients, and patients with unilateral lesion could be considered safe from CLNMs

A Study on Central Lymph Node Metastasis in 543 cN0 Papillary Thyroid Carcinoma Patients

Background. Papillary thyroid carcinoma (PTC) with central lymph node metastases (CLNMs) is common. The objective of this study was to investigate the incidence and risk factors of lymph node metastasis patients with PTC. Patients and Methods. Between January 2013 and February 2015, a retrospective study of 543 patients with PTC undergoing hemithyroidectomy or total thyroidectomy with routine central lymph node dissection (CLND) was analyzed. Clinicopathologic risk factors for CLNM were studied using univariate and multivariate analysis by SPSS 22.0 software. Results. The incidence of CLNMs in PTC patients was 38.1% (207/543). In the multivariate analysis, male gender (p<0.001, OR: 1.984), age <45 years (p<0.001, OR: 1.934), bilaterality (p=0.006, OR: 1.585), tumor size ≥0.25 cm (p=0.001, OR: 7.655), and external extension (p=0.001, OR: 7.579) were independent risk factors of CLNMs. Furthermore, in PTC patients with tumor size <0.25 cm, all 7 males and 21 patients with unilaterality were not found to have CLNMs. Conclusions. CLNMs are prevalent in the PTC patients with the following risk factors: male gender, age <45 years, bilaterality, tumor size ≥0.25 cm, and external extension. PTC patients with tumor size <0.25 cm, male patients, and patients with unilateral lesion could be considered safe from CLNMs

Silicon-Encapsulated Hollow Carbon Nanofiber Networks as Binder-Free Anodes for Lithium Ion Battery

Silicon-encapsulated hollow carbon nanofiber networks with ample space around the Si nanoparticles (hollow Si/C composites) were successfully synthesized by dip-coating phenolic resin onto the surface of electrospun Si/PVA nanofibers along with the subsequent solidification and carbonization. More importantly, the structure and Si content of hollow Si/C composite nanofibers can be effectively tuned by merely varying the concentration of dip solution. As-synthesized hollow Si/C composites show excellent electrochemical performance when they are used as binder-free anodes for Li-ion batteries (LIBs). In particular, when the concentration of resol/ethanol solution is 3.0%, the product exhibits a large capacity of 841 mAh g−1 in the first cycle, prominent cycling stability, and good rate capability. The discharge capacity retention of it was ~90%, with 745 mAh g−1 after 50 cycles. The results demonstrate that the hollow Si/C composites are very promising as alternative anode candidates for high-performance LIBs