3D Edge Research Articles

Proof of bulk-edge correspondence for band topology by Toeplitz algebra

Abstract We rigorously yet concisely prove the bulk-edge correspondence for general d-dimensional (dD) topological insulators in complex Altland-Zirnbauer classes, which states that the bulk topological number equals to the edge-mode index. Specifically, an essential formula is discovered that links the quantity expressed by Toeplitz algebra, i.e., hopping terms on the lattice with an edge, to the Fourier series on the bulk Brillouin zone. We then apply it to chiral models and utilize exterior differential calculations, instead of the sophisticated K -theory, to show that the winding number of bulk system equals to the Fredholm index of 1D edge Hamiltonian, or to the sum of edge winding numbers for higher odd dimensions. Moreover, this result is inherited to the even dimensional Chern insulators as each of them can be mapped to an odd dimensional chiral model. It is revealed that the Chern number of bulk system is identical to the spectral flow of 2D edge Hamiltonian, or to the negative sum of edge Chern numbers for higher even dimensions. Our methods and conclusions are friendly to physicists and could be easily extended to other physical scenarios.

Interpreting the edge angle efficiency of middle palaeolithic lithic assemblage with Artifact-3D – A case study Barwaniya W1, North Karanpura Valley, Eastern India

The lithic technology is directly linked with knapping skills, cognition, and economic production of a particular culture. The central objective of lithic production was to obtain desirable edge angles and sharpness for cutting/ butchering work in an efficient manner to achieve the highest utility/ efficiency and low production ratio. The angle of an edge, whether unaltered or retouched and as part of the overall tool design, is undoubtedly a parameter that influences the behavioural dimension of a lithic artefact. Thus, lithics artefacts' work efficiency or knapping efficiency depends on their working (cutting) edge angle, length of the parallel edge, centre of gravity, length of lithic, and lesser rate of edge angle damage. The present research has analysed the working edge angle of the Middle Palaeolithic Levallois assemblage from the newly discovered site of Barwaniya W1 from the North Karanpura Valley (North of Upper Damodar Basin), Jharkhand, India with the help of semi-automated 3D edge angle analysis technique. the current study scrutinises the entirety of an artefact's edge angle through automated positioning based on surface tension and a higher number of reference measurement points on the 3D scan surface of the artefact. This research has digitally measured the Middle Palaeolithic quartzite flake-based assemblage from Barwaniya W1 and their working edge angle, parallel edge (thick blunt side used for hafting), and centre of gravity with Artefact-3D software which has provided us with precise measurement without human errors and ambiguity. Further, this research has replicated the quartzite lithic assemblage of Barwaniya W1 with the Levallois reduction technique to interpret the edge angle efficiency.

High-Visibility Edge-Highlighting Visualization of 3D Scanned Point Clouds Based on Dual 3D Edge Extraction

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D edges and high-curvature regions of the scanned objects. However, traditional methods often struggle with real-world objects due to inadequate representation of soft edges (i.e., rounded edges) and excessive line clutter, impairing resolution and depth perception. To address these challenges, we propose a novel visualization method for 3D scanned point clouds based on dual 3D edge extraction and opacity–color gradation. Dual 3D edge extraction separately identifies sharp and soft edges, integrating both into the visualization. Opacity–color gradation enhances the clarity of fine structures within soft edges through variations in color and opacity, while also creating a halo effect that improves both resolution and depth perception of the visualized edges. Computation times required for dual 3D edge extraction are comparable to conventional binary statistical edge-extraction methods. Visualizations with opacity–color gradation are executable at interactive rendering speeds. The effectiveness of the proposed method is demonstrated using 3D scanned point cloud data from high-value cultural heritage objects.

Thermal and thermo-mechanical post-buckling analysis of GNP-reinforced composite laminated plates

This research utilizes isogeometric analysis (IGA) method to analyze buckling and post-buckling responses of composite laminated plates embedded with graphene nanoplatelets (GNPs) subjected to three-dimensional (3D) conduction of heat or combined 3D heat conduction and mechanical edge compression. A formulation to determine temperature profile produced by 3D conduction of heat in the GNP-reinforced laminate is established, and a new function with smooth transition form of GNP volume fraction across the thickness is presented for dictating the volume fraction of layer. Shear deformable quasi-3D theory with the von Kármán type nonlinearity which takes initial geometric deformation and thermal effect into consideration is employed to construct the nonlinear equilibrium states. Four types of GNP arrangements encompassing uniform, O, X and V shapes are considered. The IGA approach presented, through the benchmark test, is evidenced to estimate the thermal buckling temperature accurately and to successfully trace the thermal post-buckling path. Additional parametric study is carried out to scrutinize the thermal and the thermo-mechanical post-buckling features of the GNP-reinforced composite laminated plates, and the new findings are sure to be served as the benchmark solutions.

Ionic Fragmentation of the Halothane Molecule Induced by EUV and Soft X-ray Radiation.

EUV and soft X-ray-induced photofragmentation of the halothane (CF3CHBrCl) molecule has been investigated using time-of-flight mass spectrometry in the coincidence mode (PEPICO) covering the valence region and vicinity of the bromine 3d, chlorine 2p, and carbon 1s edges. Total and partial ion yields have been recorded as a function of photon energy. At lower photon energies, the heavier singly charged molecular fragments predominate in the mass spectra. On the other hand, there is a strong tendency to the atomization of the molecule at higher photon energies. Despite the different chemical environments experienced by the two carbon atoms, weak site-specific fragmentation is observed. In addition, ab initio quantum mechanical calculations at the MP2 level and a series of computations with multiconfigurational self-consistent field have been performed to describe the inner-shell states.

Upgrade of the Lyman-alpha diagnostic system on DIII-D for main chamber edge neutral studies.

The LLAMA (Lyman Alpha Measurement Apparatus) pinhole camera diagnostic had previously been deployed on DIII-D to measure radial profiles of the Lyman-α (Ly-α) deuterium neutral line brightness across the plasma boundary in the lower chamber to infer neutral deuterium density and ionization rate profiles. This system has recently been upgraded with a new diagnostic head, named ALPACA, that also encloses two pinhole cameras and duplicates the LLAMA views in the upper chamber. Similar to LLAMA, ALPACA provides two times 20 lines of sight, viewing the plasma edge on the inboard and outboard sides with a radial resolution of ∼2.5 cm (FWHM) and an effective time resolution of ∼1 ms that allows for the investigation of inter-ELM dynamics. The extended Ly-α system provides better coverage to study neutrals in experiments with various plasma shapes utilizing both the upper and lower divertors. Furthermore, post-campaign calibration of the LLAMA diagnostic has successfully been demonstrated for the first time. This was facilitated by various upgrades to the calibration set-up and detailed measurements of the emissivity distribution of the Ly-α calibration source using a pinhole collimator. It was found that the sensitivity of the inboard LLAMA pinhole camera was reduced by a factor of 2.0 ± 0.2 over the course of six months of plasma operation in 2021. The upgraded Ly-α system, equipped with improved absolute calibration, will provide key input for neutral fueling and pedestal particle transport studies and for 2D edge transport code validation on the DIII-D tokamak.

EBC-Net: 3D semi-supervised segmentation of pancreas based on edge-biased consistency regularization in dual perturbation space.

Deep learning technology has made remarkable progress in pancreatic image segmentation tasks. However, annotating 3D medical images is time-consuming and requires expertise, and existing semi-supervised segmentation methods perform poorly in the segmentation task of organs with blurred edges in enhanced CT such as the pancreas. To address the challenges of limited labeled data and indistinct boundaries of regions of interest (ROI). We propose Edge-Biased Consistency Regularization (EBC-Net). 3D edge detection is employed to construct edge perturbations and integrate edge prior information into limited data, aiding the network in learning from unlabeled data. Additionally, due to the one-sidedness of a single perturbation space, we expand the dual-level perturbation space of both images and features to more efficiently focus the model's attention on the edges of the ROI. Finally, inspired by the clinical habits of doctors, we propose a 3D Anatomical Invariance Extraction Module and Anatomical Attention to capture anatomy-invariant features. Extensive experiments have demonstrated that our method outperforms state-of-the-art methods in semi-supervised pancreas image segmentation. Moreover, it can better preserve the morphology of pancreatic organs and excel at edges region accuracy. Incorporated with edge prior knowledge, our method mixes disturbances in dual-perturbation space, which shifts the network's attention to the fuzzy edge region using a few labeled samples. These ideas have been verified on the pancreas segmentation dataset.

Edge highlighting with depth-dependent opacity gradation of laser-scanned point clouds improves the accuracy of perceived depth in transparent multi-view 3D visualizations

There is an activity called "digital archiving" in which cultural property is digitized for preservation and utilization, and transparent visualization using 3D point clouds is a valuable tool for understanding the complex internal structure of cultural property. However, when 3D data is transparently visualized, depth information may become unclear. In this paper, we investigated whether the depth perception of transparently visualized 3D objects can be improved by highlighting the 3D edges of the structures and adjusting the opacity of the edges according to the depth. In order to verify the effect of the depth-dependent 3D edges, we conducted a psychophysical experiment in which subjects were asked to report the magnitude of perceived depth for 3D structures of two cultural properties using a multi-view 3D display. The perceived depth was smaller than the simulated depth under all conditions. However, the opacity adjustment in edge highlighting mitigated the depth underestimation from an average of 69.4 to 35.5%. These results indicate that edge highlighting with opacity adjusted according to depth improves the accuracy of the perceived depth of 3D structures of cultural property visualized transparently.Graphical abstract

Monocular Depth and Ego-motion Estimation with Scale Based on Superpixel and Normal Constraints

3D perception in intelligent VR/AR and autonomous vehicles (AV) applications is critical and attracting significant attention. The self-supervised monocular depth and ego-motion estimation serves as a more intelligent learning approach that provides the required scene depth and location for 3D perception. However, the existing self-supervised learning methods suffer from scale ambiguity, boundary blur and imbalanced depth distribution, limiting the practical applications of VR/AR and AV. In this paper, we propose a new self-supervised learning framework based on superpixel and normal constraints to address these problems. Specifically, we formulate a novel 3D edge structure consistency loss to alleviate the boundary blur of depth estimation. To address the scale ambiguity of estimated depth and ego-motion, we propose a novel surface normal network for efficient camera height estimation. The surface normal network is composed of a deep fusion module and a full-scale hierarchical feature aggregation module. Meanwhile, to realize the global smoothing and boundary discriminability of the predicted normal map, we introduce a novel fusion loss which is based on the consistency constraints of the normal in edge domains and superpixel regions. Experiments are conducted on several benchmarks, and the results illustrate that the proposed approach outperforms the state-of-the-art methods in depth, ego-motion and surface normal estimation.

Enhanced efficiency of correlated photon pairs generation in silicon nitride with a low-loss 3D edge coupler

We demonstrate the generation of correlated photon pairs by using a hybrid integrated quantum photonic platform, where the dual-layer platform consists of a high-index doped silica glass (HDSG) layer to accommodate low-loss linear components and an SiN-based layer to accommodate the photon source. Leveraging the low-loss fiber coupling to the HDSG waveguide and the high nonlinearity of the SiN waveguide, we experimentally realize integrated source of photon pairs with high heralding efficiency. The directly measured photon pair rate is up to 87 KHz (corresponding to 1.74 × 10−3 pairs per pulse) when the coincidence-to-accidental ratio is greater than 10. The raw heralding efficiency can reach 18%. If the filtering loss is excluded, the heralding efficiency can further reach 29%.

Analysis and implementation of volume reflection gratings in photorefractive lithium niobate for edge enhancement.

Diffraction from volume reflection gratings written in bulk photorefractive lithium niobate is modeled for the case of longitudinally varying index modulation depths. Numerical solutions to the Helmholtz equation are found in the spatial frequency domain, leading to transfer functions for the volume reflection grating. These transfer functions are then used to show the spatial frequency filtering effect of the volume reflection grating on input light fields containing 2D spatial information. It is shown, first through simulations and then by experiment, that the 0th order transmitted beam undergoes a 2D edge enhancement.

Improving 3D edge detection for visual inspection of MRI coregistration and alignment

BackgroundVisualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. New methodWe suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). ResultsWe demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. Comparison with existing methodsCurrent methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. ConclusionThe second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

A novel 3D topological metamaterial for controllability of polarization-dependent multilayer elastic waves

The achievement of high-quality wave manipulation and energy concentration has always been considered as state-of-the-art technologies, especially for integrated photonics, acoustics, and mechanics. The exploration of the topological phase of matter provides abundant design tools for robust waveguiding that is immune to backscattering at small defects and sharp bends. Recent research has extended the elastic wave manipulation from 2D edge waveguiding to 3D planar waveguiding. However, most of them are limited to single-mode and single-frequency wave propagation along the designed plane. This paper introduces a novel 3D topological metamaterial structure whose geometrical parameters are specifically configured to obtain dual-mode topological states at distinct frequencies. Parametric studies are presented to demonstrate the controllability of bandgaps and to provide a design principle for preventing the effects of unwanted modes. Topological interface modes with either high group velocity or near-zero group velocity along the z direction are found. Full-scale finite element simulations are presented to uncover the elastic wave propagation behavior. The interesting layer-locked and layer-unlocked waveguiding based on excitation polarization and frequency for both straight path and zig-zag path are demonstrated. The outcomes of this work suggest abundant potential applications related to elastic wave control such as wave filters, energy harvesters, mechanical computers, and the like. This work may also help inspire future research on more complex and sophisticated multi-mode waveguiding in 3D spaces.

3D EDGE DETECTION BASED ON NORMAL VECTORS

Abstract. Edge detection is supported by extensive research and is part of different photogrammetric and computer vision tasks across numerous application areas. While 2D edge detection may achieve high accuracy results from several automated methods, the automation of edge detection in 3D space remains a challenge. Existing methods are often computationally demanding and heavily parameterized, leading to a lack of adaptability. In real-world applications 3D edges, representing the object boundaries and break lines, are crucial, particularly in fields such as computer vision, robotics and architecture. In this context, we present a method that automates 3D edge detection in 3D point clouds exploiting the normal vectors’ direction differences to detect finite edges, which are further pruned and grouped to edge segments and fitted to indicate the presence of a 3D edge.

Unsupervised learning-based approach for detecting 3D edges in depth maps

3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder–decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.

Edge-by-Edge Lateral Heterostructure through Interfacial Sliding.

van der Waals heterostructures (vdWHs) based on two-dimensional (2D) semiconductors have attracted considerable attention. However, the reported vdWHs are largely based on vertical device structure with large overlapping area, while the realization of lateral heterostructures contacted through 2D edges remains challenging and is majorly limited by the difficulties of manipulating the lateral distance of 2D materials at nanometer scale (during transfer process). Here, we demonstrate a simple interfacial sliding approach for realizing an edge-by-edge lateral contact. By stretching a vertical vdWH, two 2D flakes could gradually slide apart or toward each other. Therefore, by applying proper strain, the initial vertical vdWH could be converted into a lateral heterojunction with intimately contacted 2D edges. The lateral contact structure is supported by both microscope characterization and in situ electrical measurements, exhibiting carrier tunneling behavior. Finally, this approach can be extended to 3D thin films, as demonstrated by the lateral 2D/3D and 3D/3D Schottky junction.

Accurate and semantic 3D reconstruction of maize leaves

The rapid advances in equipment and technology for crop three-dimensional (3D) data acquisition have enabled the high-throughput and high-quality acquisition of crop 3D data. However, the challenge now lies in comprehending unstructured crop 3D data and extracting phenotypes and knowledge. Extracting semantic feature points from plant leaves and achieving high-precision 3D reconstruction is a prevalent and formidable issue in this field. In this study, we present an accurate method for semantic 3D reconstruction of maize leaves. The algorithm utilizes 3D point cloud data of maize leaves as the initial input. Subsequently, the semantic 3D reconstruction of maize leaves is achieved by means of point cloud preprocessing, initial triangular meshing, hole filling, remeshing, mesh parameterization, two-dimensional (2D) edge detection, 2D semantic point interpolation, 3D semantic point selection, mesh generation, and optimization of feature-point position. A total of 56 leaves from five different maize cultivars were reconstructed utilizing this method. The results indicate that the average distance error index dCDMP between the reconstructed mesh vertices and the input point cloud is less than 0.3 cm2 for all five cultivars, and the mean of the maximum distance error index dHDMP for each cultivar is within the range of 0.5–0.8 cm, indicating it is high-accuracy. The visualization of leaves shows good consistency between the reconstructed mesh and corresponding point cloud. Additionally, the reconstructed 3D mesh of the leaves exhibits enhanced triangular facet consistency. Each vertex can be precisely identified as a leaf vein point, leaf edge point, leaf tip point, or leaf surface point, and includes semantic data such as the position on the leaf blade, which can be directly employed for phenotype estimations. High-precision reconstruction of 3D models of crop organs that include semantic information and cultivar resolution yields significant benefits. This technology can improve the resolution and accuracy of crop morphological and structural research, and it is also crucial for crop phenomics, functional-structural plant modelling, and fusion research between plant phenotyping and crop models.

Instance Segmentation Frustum–PointPillars: A Lightweight Fusion Algorithm for Camera–LiDAR Perception in Autonomous Driving

The fusion of camera and LiDAR perception has become a research focal point in the autonomous driving field. Existing image–point cloud fusion algorithms are overly complex, and processing large amounts of 3D LiDAR point cloud data requires high computational power, which poses challenges for practical applications. To overcome the above problems, herein, we propose an Instance Segmentation Frustum (ISF)–PointPillars method. Within the framework of our method, input data are derived from both a camera and LiDAR. RGB images are processed using an enhanced 2D object detection network based on YOLOv8, thereby yielding rectangular bounding boxes and edge contours of the objects present within the scenes. Subsequently, the rectangular boxes are extended into 3D space as frustums, and the 3D points located outside them are removed. Afterward, the 2D edge contours are also extended to frustums to filter the remaining points from the preceding stage. Finally, the retained points are sent to our improved 3D object detection network based on PointPillars, and this network infers crucial information, such as object category, scale, and spatial position. In pursuit of a lightweight model, we incorporate attention modules into the 2D detector, thereby refining the focus on essential features, minimizing redundant computations, and enhancing model accuracy and efficiency. Moreover, the point filtering algorithm substantially diminishes the volume of point cloud data while concurrently reducing their dimensionality, thereby ultimately achieving lightweight 3D data. Through comparative experiments on the KITTI dataset, our method outperforms traditional approaches, achieving an average precision (AP) of 88.94% and bird’s-eye view (BEV) accuracy of 90.89% in car detection.

AI-driven real-time failure detection in additive manufacturing

The optimisation of 3D printing parameters for manufacturing biomedical devices is an emerging interdisciplinary field that incorporates artificial intelligence techniques such as machine learning and deep learning. In this particular study, the focus is on the fabrication of biocompatible finger splints using digital light processing 3D printing technology, followed by UV curing, to evaluate their quality. By leveraging vibration data from printers, which cannot be captured through visual inspection of layer defects, this study aims to develop a predictive model for assessing the failures of printed parts. Here, a closed-loop detection system is proposed to identify failure phenomena in 3D resin printing, combining both cloud and edge computing technologies to effectively detect and address potential failures in the printing process.

Multiscale lung nodule segmentation based on 3D coordinate attention and edge enhancement

<abstract> <p>An important prerequisite for improving the reliability of lung cancer surveillance and clinical interventions is accurate lung nodule segmentation. Although deep learning is effective at performing medical image segmentation, lung CT image heterogeneity, nodule size, shape, and location variations, convolutional localized feature extraction characteristics, the receptive field limitations of continuous downsampling, lesion edge information losses, fuzzy boundary segmentation challenges, and the low segmentation accuracy achieved when segmenting lung CT images using deep learning remain. An edge-enhanced multiscale Sobel coordinate attention-atrous spatial convolutional pooling pyramid V-Net (SCA-VNet) algorithm for lung nodule segmentation was proposed to solve these problems. First, a residual edge enhancement module was designed, which was used to enhance the edges of the original data. Using an edge detection operator in combination with a residual module, this module could reduce data redundancy and alleviate the gray level similarity between the foreground and background. Then, a 3D atrous spatial convolutional pooling pyramid module set different expansion rates, which could obtain feature maps under different receptive fields and capture the multiscale information of the segmentation target. Finally, a three-dimensional coordinate attention network (3D CA-Net) module was added to the encoding and decoding paths to extract channel weights from multiple dimensions. This step propagated the spatial information in the coding layer to the subsequent layers, and it could reduce the loss of information during the forward propagation process. The proposed method achieved a Dice coefficient of 87.50% on the lung image database consortium and image database resource initiative (LIDC-IDRI). It significantly outperformed the existing lung nodule segmentation models (UGS-Net, REMU-Net, and multitask models) and compared favorably with the Med3D, CENet, and PCAM_Net segmentation models in terms of their Dice coefficients, which were 3.37%, 2.2%, and 1.43%, respectively. The experimental results showed that the proposed SCA-VNet model attained improved lung nodule segmentation accuracy and laid a good foundation for improving the early detection rate of lung cancer.</p> </abstract>