3D Shape Research Articles

Active shape programming drives Drosophila wing disc eversion.

How complex 3D tissue shape emerges during animal development remains an important open question in biology and biophysics. Here, we discover a mechanism for 3D epithelial shape change based on active, in-plane cellular events that is analogous to inanimate "shape programmable" materials, which undergo blueprinted 3D shape transformations from in-plane gradients of spontaneous strains. We study eversion of the Drosophila wing disc pouch, when the epithelium transforms from a dome into a curved fold, quantifying 3D tissue shape changes and mapping spatial patterns of cellular behaviors on the evolving geometry using cellular topology. Using a physical model inspired by shape programming, we find that active cell rearrangements are the major contributor to pouch eversion and validate this conclusion using a knockdown of MyoVI, which reduces rearrangements and disrupts morphogenesis. This work shows that shape programming is a mechanism for animal tissue morphogenesis and suggests that patterns in nature could present design strategies for shape-programmable materials.

Tailored Fabrication of 3D Nanopores Made of Dielectric Oxides for Multiple Nanoscale Applications.

Solid-state nanopores are a key platform for single-molecule detection and analysis that allow engineering of their properties by controlling size, shape, and chemical functionalization. However, approaches relying on polymers have limits for what concerns hardness, robustness, durability, and refractive index. Nanopores made of oxides with high dielectric constant would overcome such limits and have the potential to extend the suitability of solid-state nanopores toward optoelectronic technologies. Here, we present a versatile method to fabricate three-dimensional nanopores made of different dielectric oxides with convex, straight, and concave shapes and demonstrate their functionality in a series of technologies and applications such as ionic nanochannels, ionic current rectification, memristors, and DNA sensing. Our experimental data are supported by numerical simulations that showcase the effect of different shapes and oxide materials. This approach toward robust and tunable solid-state nanopores can be extended to other 3D shapes and a variety of dielectrics.

Clinical and Micro-CT analyses of Vertical Guided Bone Regeneration of Mandible using a d-PTFE Membrane, Autogenous Bone, and High-Temperature Processed Xenograft- A Case Series Study.

Purpose: To evaluate the efficacy of vertical guided bone regeneration (GBR) in the mandible utilizing a non-resorbable membrane and a bone graft combination of autogenous bone chips, and high-temperature processed (HTP) xenograft, through CT scans and microCT analysis. Materials and Methods: Patients underwent vertical ridge augmentation procedures prior to implant placement. The surgical procedure included flap elevation and placement of a bone graft comprising a 1:1 combination of autogenous posterior mandible-derived bone chips, and HTP xenograft graft particles covered with a d-PTFE membrane trimmed to suit the 3D shape of the bone defect. This was fastened securely with titanium screws and pins, and a layer of native collagen membrane. Post-operative complications and ridge measurements were assessed. Pre bone augmentation and pre implant placement bone parameters were obtained from CT scans. Biopsy specimens collected during implantation were examined by microCT. Results: All 13 study procedures were successful without any complications. The results revealed average vertical and horizontal bone gains of 3.35 mm and 5.15 mm respectively. A total of 33 implants were successfully placed in the augmented areas, without the need for further bone augmentation. MicroCT analysis revealed 48% bone, 15% filler material, and 37% non-calcified tissue in the augmented region compared to 65% bone, 3% filler material, and 32% non-calcified tissue in the pristine bone. Conclusions: A mixture of autogenous bone and HTP xenograft, covered with a d-PTFE membrane and a layer of native collagen membrane is effective for vertical GBR.

3D Morphological Feature Quantification and Analysis of Corn Leaves.

Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage. We propose quantitative calculation methods for 13 3D leaf shape features, such as the leaf length, 3D leaf area, leaf inclination angle, blade-included angle, blade self-twisting, blade planarity, and margin amplitude. Correlation analysis, cluster analysis, and heritability analysis were conducted among the 13 leaf traits. Leaf morphology differences among subpopulations of the inbred lines were also analyzed. The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces, and the genetic analysis revealed that 84.62% of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3. However, the majority of 3D leaf shape traits were strongly affected by environmental conditions. Overall, this study quantitatively investigated 3D leaf shape in corn, providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics, phenotypes, and the environment.

Compensating slice emittance growth in high brightness photoinjectors using sacrificial charge

Achieving maximum electron beam brightness in photoinjectors requires detailed control of the 3D bunch shape and precise tuning of the beam focusing. Even in state-of-the-art designs, slice emittance growth due to nonlinear space charge forces and partial nonlaminarity often remains non-negligible. In this work, we introduce a new means to linearize the transverse slice phase space: a sacrificial portion of the bunch’s own charge distribution, formed into a wavebroken shock front by highly nonlinear space charge forces within the gun, whose downstream purpose is to dynamically linearize the desired bunch core. We show that linearization of an appropriately prepared bunch can be achieved via strongly nonlaminar focusing of the sacrificial shock front, while the inner core focuses laminarly. This leads to a natural spatial separation of the two distributions: a dense core surrounded by a diffuse halo of sacrificial charge that can be collimated. Multiobjective genetic algorithm optimizations of the ultracompact x-ray free electron laser injector employ this concept, and we interpret it with an analytic model that agrees well with the simulations. In simulation, we demonstrate a final bunch charge of 100 pC, peak current ∼30 A, and a sacrificial charge of 150 pC (250 pC total emitted from cathode) with normalized emittance growth of <20 nm rad due to space charge. This implies a maximum achievable brightness approximately an order of magnitude greater than existing free electron laser injector designs. Published by the American Physical Society 2024

Spatial coding strategy for dual-frequency phase-shifting profilometry

Dual-frequency fringe projection techniques have been broadly utilized for three-dimensional (3D) shape recovery. However, a pervasive challenge across these methodologies lies in the precise implementation of high-speed measurements. This paper explores a spatial coding strategy designed for the dual-frequency phase unwrapping method to tackle this. The strategy mandates the utilization of only four patterns, encapsulating the phase information of both high and low frequencies. The codewords are strategically embedded within the low-frequency phase, facilitating the acquisition of an accurate absolute phase map. A robust decoding algorithm engineered to ensure dependable phase unwrapping, particularly in scenarios characterized by a substantial number of high-frequency cycles. The proposed method exhibits superior noise immunity and enhances accuracy when compared with the previous dual-frequency phase-shifting methodology. Simulations and experiments demonstrate the robustness and efficiency of the proposed strategy.

Real-time motion-induced error reduction for phase-shifting profilometry with projection points tracking method

Fringe projection profilometry is a significant method for three-dimensional measurement due to its non-contact and high accuracy. However, the motion-induced error will lead to the loss of measurement accuracy in dynamic scenes due to the disruption of the phase measurement process. In this paper, we introduce a novel motion error model that considers object motion causes the misalignment of the projection points on the camera, and propose the projection points tracking method to reduce the motion-induced error. First, the speckle pattern is added to projection sequences, after which the projection point displacements are determined using the digital image correlation (DIC) method between the adjacent speckle patterns. Finally, the fringe patterns are corrected using the image remapping method to calculate the 3D shape. Quantitative analysis and dynamic measurement experiments verify the feasibility of the proposed method. Different from 3D-DIC, we use one camera-projector system to realize high-accuracy measurement in dynamic scenes.

An analytical model for wrinkle-free forming of composite laminates

Double Diaphragm Forming (DDF) of 3D shapes significantly increases production rates for laminated composite parts when compared with Automated Fibre Placement. However, it is prone to the occurrence of wrinkling defects, which can lead either to concession of part strength or expensive and wasteful part scrappage. Finite element simulation of the process is computationally costly and not suited to preliminary design for manufacture. In this work a novel analytical model is presented, based on obtaining the wrinkle profile via energy minimisation using a Rayleigh–Ritz approach, which enables rapid mechanics-based assessment of formability for the design of wrinkle-free composite products. The new modelling approach is validated against eight experimental DDF forming trials performed over C-shaped tools with double curvature using both dry Non-Crimped Fabric and unidirectional prepreg material. The model successfully predicts the three cases for which wrinkling is absent and the five cases where it occurs.

Enhanced Analysis of Ice Accretion on Rotating Blades of Horizontal-Axis Wind Turbines Using Advanced 3D Scanning Technology

This study investigated the meteorological conditions leading to ice formation on wind turbines in a coastal mountainous area. An enhanced ice formation similarity criterion was developed for the experimental design, utilizing a scaled-down model of a 1.5 MW horizontal-axis wind turbine in icing wind tunnel tests. Three-dimensional ice shapes on the rotating blades were obtained and scanned using advanced 3D laser measurement technology. Post-processing of the scanned data facilitated the construction of solid models of the ice-covered blades. This study analyzed the maximum ice thickness, ice-covered area, and dimensionless parameters such as the maximum dimensionless ice thickness and dimensionless ice-covered area along the blade. Under the experimental conditions, the maximum ice thickness reached 0.5102 m, and the ice-covered area extended up to 0.5549 m2. The dimensionless maximum ice thickness and dimensionless ice-covered area consistently increased along the blade direction. Our analysis of 3D ice shape characteristics and the ice volume under different test conditions demonstrated that wind speed and the liquid water content (LWC) are critical factors affecting ice formation on blade surfaces. For a constant tip speed ratio, higher wind speeds and a greater LWC resulted in increased ice volumes on the blade surfaces. Specifically, increasing the wind speed can augment the ice volume by up to 57.2%, while increasing the LWC can enhance the ice volume by up to 149.2% under the experimental conditions selected in this study.

Detection and estimation of 3D shape of waste based on neural networks

Abstract This article considers the possibility of using neural networks to detect and recognize different types of waste and determine its shape. The developed algorithm can be used to automate the waste sorting process for further sorting, recycling or disposal. This article deals with the detection of 28 classes of objects made of iron, plastic, cardboard and glass with varying degrees of deformation and damage. The detection is based on the YOLO8 neural network. The quality of the YOLOv8 model was evaluated on test data using the following metrics: box_loss, cls_loss, dfl_loss, mAP50, mAP50-95, precision, recall. After training the neural network, the YOLOv8 metrics on the test data are: box_loss=1.14, cls_loss=2.21, dfl_loss=1.21, mAP50=0.48, mAP50-95=0.38, precision=0.60, recall=0.44. The 3d shape detection is done after analyzing the image by YOLOv8 model based on GLPN neural network.

Nanoporous, Ultrastiff, and Transparent Plastic-like Polymer Hydrogels Enabled by Hydrogen Bonding-Induced Self-Assembly.

Most natural supporting tissues possess both exceptional mechanical strength, a significant amount of water, and the anisotropic structure, as well as nanoscale assembly. These properties are essential for biological processes, but have been challenging to emulate in synthetic materials. In an effort to achieve simultaneous improvement of these trade-off features, a hydrogen bonding-induced self-assembly strategy was introduced to create nanoporous plastic-like polymer hydrogels. Multiple hydrogen bonding-mediated networks and nanoporous orientation structures endow transparent hydrogels with remarkable mechanical robustness. They exhibit Young's modulus of up to 223.7 MPa and a breaking strength of up to 10.3 MPa, which are superior to those of most common polymer hydrogels. The uniform porous nanostructures of hydrogen-bonded hydrogels contribute to a significantly larger specific surface area compared to conventional hydrogels. This allows for the retention of high mechanical properties in environments with a high water content of 70 wt %. A rubbery stage is observed during the heating process, which can reverse and reshape the manufacture of objects with various desired 2D or 3D shapes using techniques such as origami and kirigami. Finally, as a proof-of-concept, the outstanding mechanical properties of poly(MAA-co-AA-co-NVCL) hydrogel, combined with its high water content, make it suitable for applications such as smart temperature monitors, multilevel information anticounterfeiting, and artificial muscles.

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.

Improving Robotic Fruit Harvesting Within Cluttered Environments Through 3D Shape Completion

Improving Robotic Fruit Harvesting Within Cluttered Environments Through 3D Shape Completion

ANISE: Assembly-Based Neural Implicit Surface Reconstruction.

We present ANISE, a method that reconstructs a 3D shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape is formulated as an assembly of neural implicit functions, each representing a different part instance. In contrast to previous approaches, the prediction of this representation proceeds in a coarse-to-fine manner. Our model first reconstructs a structural arrangement of the shape in the form of geometric transformations of its part instances. Conditioned on them, the model predicts part latent codes encoding their surface geometry. Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape. We demonstrate that, when performing reconstruction by decoding part representations into implicit functions, our method achieves state-of-the-art part-aware reconstruction results from both images and sparse point clouds. When reconstructing shapes by assembling parts retrieved from a dataset, our approach significantly outperforms traditional shape retrieval methods even when significantly restricting the database size. We present our results in well-known sparse point cloud reconstruction and single-view reconstruction benchmarks.

3D-ISRNet:Generating 3D point clouds through image similarity retrieval in a complex background from a single image

Reconstructing the 3D shape of objects using limited information from a single image is a complex and necessary task. The existing methods for reconstructing point clouds from a single image haven't effectively addressed the issue of background interference in the input image, thus greatly limited their practical application. To address the underlying issue, this paper proposes a 3D-ISRNet aiming to enhance the accuracy of point cloud reconstruction through image similarity retrieval. The 3D-ISRNet retrieves point clouds similar to the object and employs spatial and channel attention mechanisms for image encoding. This aims to enhance the features of the reconstruction area and improve reconstruction accuracy. After the fusion of retrieved point clouds with image features, the combined data undergoes a symmetric encoder to generate the reconstructed point clouds, thereby alleviating the constraints imposed by the initial point clouds on the reconstruction results. 3D-ISRNet demonstrates favorable results on public and real-world datasets.

Characteristic-Preserving Latent Space for Unpaired Cross-Domain Translation of 3D Point Clouds.

This article aims at unpaired shape-to-shape transformation for 3D point clouds, for instance, turning a chair to its table counterpart. Recent work for 3D shape transfer or deformation highly relies on paired inputs or specific correspondences. However, it is usually not feasible to assign precise correspondences or prepare paired data from two domains. A few methods start to study unpaired learning, but the characteristics of a source model may not be preserved after transformation. To overcome the difficulty of unpaired learning for transformation, we propose alternately training the autoencoder and translators to construct shape-aware latent space. This latent space based on novel loss functions enables our translators to transform 3D point clouds across domains and maintain the consistency of shape characteristics. We also crafted a test dataset to objectively evaluate the performance of point-cloud translation. The experiments demonstrate that our framework can construct high-quality models and retain more shape characteristics during cross-domain translation compared to the state-of-the-art methods. Moreover, we also present shape editing applications with our proposed latent space, including shape-style mixing and shape-type shifting, which do not require retraining a model.

Flow dynamics and heat transfer characteristics of flows past a zigzag cylinder in a bounded domain

A comprehensive numerical and experimental study is conducted to investigate the flow dynamics and heat transfer characteristics of flows past a novel wavy-axis circular cylinder placed symmetrically between two parallel walls. For comparison, the flow past a corresponding straight-axis cylinder within the same bounded domain is also studied. The investigations were primarily conducted at Reynolds numbers of 626 and 680, based on the average velocity and diameter of the cylinder, with a blockage ratio of 0.42, based on the widest area of the channel. At these flow conditions, the straight cylinder expectedly showed a “reverse” von Kármán type wake. However, the special zigzag cylinder showed fundamentally distinct flow patterns at the wake leading to a significant drag reduction and a heat transfer enhancement, compared to the performance of the straight cylinder. The pressure drop of the zigzag cylinder is 14 % and 19.4 % lower than that of the straight cylinder, with a higher convective heat transfer coefficient of 24 % and 9 %, respectively, for Reynolds numbers of 626 and 680. The substantial drag reduction is attributed to the formation of steady flow pattern in the wake indicating the suppression of unsteady vortex shedding. The heat transfer enhancement was mainly caused by the formation of elongated vortical structures principally aligned in the streamwise direction. The creation of these streamwise vortex tubes tends to more efficiently mix the fluid between the near-wall area and the core region of the channel than the near-planar vortex shedding associated to the straight cylinder. It was shown that the 3D shape of the zigzag cylinder generates a spanwise velocity component, which results in the creation of a dominant streamwise vorticity component that eventually develops into sustained vortex tubes.

Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization

AbstractWe introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state‐of‐the‐art approaches for this task often rely on the watertightness of the input [LWS*15; PWG*19; PWG*19] or suffer from substantial computational costs [DLX*22; CD23], thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high‐accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water‐tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis.

3D printing of reprogrammable liquid crystal elastomers with exchangeable boronic ester bonds

Liquid crystal elastomers (LCEs) are a kind of soft actuating materials with large reversible deformation ability, which can work as the “motor” to exhibit complex deformations and drive the locomotion of soft robots. The deformation of LCEs depends on the three-dimensional (3D) shape of whole structure and alignment patterns of mesogens. Various methods have been employed to fabricate the LCE structure with desired shapes and mesogen alignments. However, conventional 3D printed LCEs require continuous thermal energy input to maintain their actuated shapes. The LCEs cannot be reprocessed and reprogrammed once cured. Herein, we introduce dynamic boronic ester bonds into the ink, with which the printed LCE structures are capable of being reprogrammed from polydomain into monodomain state and vice versa. We further explore the effects of printing parameters and content of dynamic covalent bonds on the actuation performance and reprogramming ability. The actuated shape could be well predicted with finite element method. The dynamic printable LCEs developed here offer new strategy and large design space for LCE structures.

Cut‐Cell Microstructures for Two‐scale Structural Optimization

AbstractTwo‐scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the object partitioned into relatively coarse cells, and then replace each cell with microstructure geometry that mimics these material properties. In this paper, we focus on adapting this approach to complex shapes in situations when preserving the shape's surface is essential.Our approach extends any regular (i.e. defined on a regular lattice grid) microstructure family to complex shapes, by enriching it with tiles adapted to the geometry of the cut‐cell. We propose a fully automated and robust pipeline based on this approach, and we show that the performance of the regular microstructure family is only minimally affected by our extension while allowing its use on 2D and 3D shapes of high complexity.