Template Mesh Research Articles

Mesh registration-based boundary element method of head-related transfer function: unification of hair impedance boundary conditions

In the calculation of Head-Related Transfer Functions (HRTFs) using the Boundary Element Method (BEM), the head surface of a subject needs to be discretized into boundary elements with specific impedance. To simplify HRTF calculation, a method for coupling Mesh Registration and BEM (MR-BEM) is proposed in this work, which means that a template mesh for calculation is established to register and generate the calculation mesh of a new head model, to replace the tedious operations of boundary conditions configuration. To validate the proposed method, the widely-used KEMAR artificial head model was adopted to compare the HRTFs of two groups of calculation meshes manually assigned and automatically registered impedance boundary conditions, respectively. Based on the inherent similarities in head shape and hair regions across individuals, the non-rigid impedance was set only for the hair region. Although the reflections from the non-rigid impedance of hair and clothes were shielded by pinnae in practice, this work just aims to validate the feasibility of the MR-BEM, which is significant for the simplification of BEM-based HRTF calculation.

Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.

TailorMe: Self‐Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model

AbstractHuman shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limited localized shape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self‐supervised fashion. The resulting TailorMe model enables shape sampling, localized shape manipulation, and fast inference from given surface scans.

DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.

LinFlo-Net: A Two-Stage Deep Learning Method to Generate Simulation Ready Meshes of the Heart.

We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this approach, our framework is designed to minimize mesh self-penetration, which typically arises when deforming surface meshes separated by small distances. We achieve this by using a two-stage diffeomorphic deformation process along with a novel loss function derived from the kinematics of motion that penalizes surface contact and interpenetration. Our model demonstrates comparable accuracy with state-of-the-art methods while additionally producing meshes free of self-intersections. The resultant meshes are readily usable in physics based simulation, minimizing the need for postprocessing and cleanup.

Boundary constrained quadrilateral mesh generation based on domain decomposition and templates

Compared to triangular or mixed meshes, pure quadrilateral meshes generally offer higher accuracy and computational efficiency in numerical simulations. Recently, the main obstacles in the generation of quadrilateral meshes have revolved around the identification of mesh singularities and the subsequent decomposition of the domain. In this study, a novel quadrilateral mesh generation method based on domain decomposition and templates is presented. The basic idea is to decompose the domain by separating the mesh singularities rather than identifying their precise locations. Surfaces composed of a set of surface patches are meshed independently. For each surface patch, the problem domain is divided into several simple polygonal subregions, including quadrangles, triangles, and pentagons, using constrained Delaunay triangulation (CDT) and recursive bisections. Subsequently, mesh templates with a limited number of singularities are employed to rapidly generate high-quality quadrilateral meshes over those subregions. By constraining the boundary discretization of each surface patch, mesh conformability is automatically achieved. An integer linear programming equation is presented to ensure that the total number of boundary nodes on each surface is an even value. Several experimental results are presented and discussed, demonstrating the reliability and efficiency of the proposed method.

ACCURACY EVALUATION OF AUTOMATIC SEGMENTATION IN BIOMEDICAL IMAGES: 3D SPATIAL MAPPING OF SEGMENTATION ERRORS

Currently implemented accuracy metrics in open-source libraries for segmentation by supervised machine learning are typically one-dimensional scores [1]. While extremely relevant to evaluate applicability in clinics, anatomical location of segmentation errors is often neglected.This study aims to include the three-dimensional (3D) spatial information in the development of a novel framework for segmentation accuracy evaluation and comparison between different methods.Predicted and ground truth (manually segmented) segmentation masks are meshed into 3D surfaces. A template mesh of the same anatomical structure is then registered to all ground truth 3D surfaces. This ensures all surface points on the ground truth meshes to be in the same anatomically homologous order. Next, point-wise surface deviations between the registered ground truth mesh and the meshed segmentation prediction are calculated and allow for color plotting of point-wise descriptive statistics. Statistical parametric mapping includes point-wise false discovery rate (FDR) adjusted p-values (also referred to as q-values).The framework reads volumetric image data containing the segmentation masks of both ground truth and segmentation prediction. 3D color plots containing descriptive statistics (mean absolute value, maximal value,…) on point-wise segmentation errors are rendered. As an example, we compared segmentation results of nnUNet [2], UNet++ [3] and UNETR [4] by visualizing the mean absolute error (surface deviation from ground truth) as a color plot on the 3D model of bone and cartilage of the mean distal femur.A novel framework to evaluate segmentation accuracy is presented. Output includes anatomical information on the segmentation errors, as well as point-wise comparative statistics on different segmentation algorithms. Clearly, this allows for a better informed decision-making process when selecting the best algorithm for a specific clinical application.

THORACOLUMBAR SPINE PATIENT-SPECIFIC FINITE ELEMENT MODEL REPOSITORY WITH ONLINE USER-INTERFACE PLATFORM

Adult Spine Deformity (ASD) is a degenerative condition of the adult spine leading to altered spine curvatures and mechanical balance. Computational approaches, like Finite Element (FE) Models have been proposed to explore the etiology or the treatment of ASD, through biomechanical simulations. However, while the personalization of the models is a cornerstone, personalized FE models are cumbersome to generate. To cover this need, we share a virtual cohort of 16807 thoracolumbar spine FE models with different spine morphologies, presented in an online user-interface platform (SpineView). To generate these models, EOS images are used, and 3D surface spine models are reconstructed. Then, a Statistical Shape Model (SSM), is built, to further adapt a FE structured mesh template for both the bone and the soft tissues of the spine, through mesh morphing. Eventually, the SSM deformation fields allow the personalization of the mean structured FE model, leading to generate FE meshes of thoracolumbar spines with different morphologies. Models can be selectively viewed and downloaded through SpineView, according to personalized user requests of specific morphologies characterized by the geometrical parameters: Pelvic Incidence; Pelvic Tilt; Sacral Slope; Lumbar Lordosis; Global Tilt; Cobb Angle; and GAP score. Data quality is assessed using visual aids, correlation analyses, heatmaps, network graphs, Anova and t-tests, and kernel density plots to compare spinopelvic parameter distributions and identify similarities and differences. Mesh quality and ranges of motion have been assessed to evaluate the quality of the FE models. This functional repository is unique to generate virtual patient cohorts in ASD.Acknowledgements: European Commission (MSCA-TN-ETN-2020-Disc4All-955735, ERC-2021-CoG-O-Health-101044828)

Fast and Simple Statistical Shape Analysis of Pregnant Women Using Radial Deformation of a Cylindrical Template.

Non-rigid deformation of a template to fit 3D scans of human subjects is widely used to develop statistical models of 3D human shapes and poses. Complex optimization problems must be solved to use these models to parameterize scans of pregnant women, thus limiting their use in antenatal point-of-care tools in low-resource settings. Moreover, these models were developed using datasets that did not contain any 3D scans of pregnant women. In this study, we developed a statistical shape model of the torso of pregnant women at greater than 36 weeks of gestation using fast and simple vertex-based deformation of a cylindrical template constrained along the radial direction. The 3D scans were pre-processed to remove noisy outlier points and segment the torso based on anatomical landmarks. A cylindrical template mesh was then fitted onto the segmented scan of the torso by moving each vertex of in the direction of the radial vector. This process is computationally inexpensive taking only 14.80 seconds to deform a template with 9090 vertices. Principal component analysis (PCA) was performed on the deformed vertex co-ordinates to find the directions of maximum variance. The first 10 principal vectors of our model explained 79.03% of the total variance and reconstructed unseen scans with a mean error of 2.43 cm. We also used the PCA weights of the first 10 principal vectors to accurately predict anthropometric measurements of the pregnant women.

Unsupervised Anthropometric Measurements from 3D Scans Using Automated Template Registration

Three dimensional digital avatars can be valuable assets in various industrial fields: from telepresence, entertainment, fashion, to wellness and medicine. Accurate geometric representations of a person’s body shape are crucial for extracting reliable anthropometric information applicable in contexts ranging from virtual try-ons to medical-grade applications, where precision can significantly impact outcomes. This paper presents two different automated procedures to perform anthropometrics measurements, both based on an non-rigid, human template mesh registration method that requires no human supervision, centered around a custom human body model. Both procedures were evaluated using datasets from six participants, comparing a fully automated registration with a manual registration performed by a trained operator. The results, analyzed and benchmarked against manual digital tape measurements, confirm that the accuracy of the automated methods aligns closely with ground truth, affirming the reliability of these measurements across diverse body shapes. This highlights the potential of fully automated methods in applications that require precise body measurements.

NerfCap: Human Performance Capture With Dynamic Neural Radiance Fields.

This paper addresses the challenge of human performance capture from sparse multi-view or monocular videos. Given a template mesh of the performer, previous methods capture the human motion by non-rigidly registering the template mesh to images with 2D silhouettes or dense photometric alignment. However, the detailed surface deformation cannot be recovered from the silhouettes, while the photometric alignment suffers from instability caused by appearance variation in the videos. To solve these problems, we propose NerfCap, a novel performance capture method based on the dynamic neural radiance field (NeRF) representation of the performer. Specifically, a canonical NeRF is initialized from the template geometry and registered to the video frames by optimizing the deformation field and the appearance model of the canonical NeRF. To capture both large body motion and detailed surface deformation, NerfCap combines linear blend skinning with embedded graph deformation. In contrast to the mesh-based methods that suffer from fixed topology and texture, NerfCap is able to flexibly capture complex geometry and appearance variation across the videos, and synthesize more photo-realistic images. In addition, NerfCap can be pre-trained end to end in a self-supervised manner by matching the synthesized videos with the input videos. Experimental results on various datasets show that NerfCap outperforms prior works in terms of both surface reconstruction accuracy and novel-view synthesis quality.

Positioning errors of anatomical landmarks identified by fixed vertices in homologous meshes

BackgroundHuman movement analysis is usually achieved by tracking markers attached to anatomical landmarks with photogrammetry. Such marker-based systems have disadvantages that have led to the development of markerless procedures, although their accuracy is not usually comparable to that of manual palpation procedures. New motion acquisition systems, such as 3D temporal scanners, provide homologous meshes that can be exploited for this purpose. Research questionCan fixed vertices of a homologous mesh be used to identify anatomical landmarks with an accuracy equivalent to that of manual palpation? MethodsWe used 3165 human shape scans from the CAESAR dataset, with labelled locations of anatomical landmarks. First, we fitted a template mesh to the scans, and assigned a vertex of that mesh to 53 anatomical landmarks in all subjects. Then we defined a nominal vertex for each landmark, as the more centred vertex out of the set assigned for that landmark. We calculated the errors of the template-fitting and the nominal vertex determination procedures, and analysed their relationship to subject’s sex, height and body mass index, as well as their size compared to manual palpation errors. ResultsThe template-fitting errors were below 5 mm, and the nominal vertex determination errors reached maximum values of 24 mm. Except for the trochanter, those errors were the same order of magnitude or smaller than inter-examiner errors of lower limb landmarks. Errors increased with height and body mass index, and were smaller for men than for women of the same height and body mass index. SignificanceWe defined a set of vertices for 53 anatomical landmarks in a homologous mesh, which yields location errors comparable to those obtained by manual palpation for the majority of landmarks. We also quantified how the subject’s sex and anthropometric features can affect the size of those errors.

A Parametric Thoracic Spine Model Accounting for Geometric Variations by Age, Sex, Stature, and Body Mass Index

<div>In this study, a parametric thoracic spine (T-spine) model was developed to account for morphological variations among the adult population. A total of 84 CT scans were collected, and the subjects were evenly distributed among age groups and both sexes. CT segmentation, landmarking, and mesh morphing were performed to map a template mesh onto the T-spine vertebrae for each sampled subject. Generalized procrustes analysis (GPA), principal component analysis (PCA), and linear regression analysis were then performed to investigate the morphological variations and develop prediction models. A total of 13 statistical models, including 12 T-spine vertebrae and a spinal curvature model, were combined to predict a full T-spine 3D geometry with any combination of age, sex, stature, and body mass index (BMI). A leave-one-out root mean square error (RMSE) analysis was conducted for each node of the mesh predicted by the statistical model for every T-spine vertebra. Most of the RMSEs were less than 2 mm across the 12 vertebral levels, indicating good accuracy. The presented parametric T-spine model can serve as a geometry basis for parametric human modeling or future crash test dummy designs to better assess T-spine injuries accounting for human diversity.</div>

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

AvatarReX: Real-time Expressive Full-body Avatars

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face together, but also supports real-time animation and rendering. To this end, we propose a compositional avatar representation, where the body, hands and the face are separately modeled in a way that the structural prior from parametric mesh templates is properly utilized without compromising representation flexibility. Furthermore, we disentangle the geometry and appearance for each part. With these technical designs, we propose a dedicated deferred rendering pipeline, which can be executed at a real-time framerate to synthesize high-quality free-view images. The disentanglement of geometry and appearance also allows us to design a two-pass training strategy that combines volume rendering and surface rendering for network training. In this way, patch-level supervision can be applied to force the network to learn sharp appearance details on the basis of geometry estimation. Overall, our method enables automatic construction of expressive full-body avatars with real-time rendering capability, and can generate photo-realistic images with dynamic details for novel body motions and facial expressions.

HyperGraph based human mesh hierarchical representation and reconstruction from a single image

Reconstructing 3D human mesh from monocular images has been extensively studied. However, the existing non-parametric reconstruction methods are inefficient when modeling vertex relationship concerning human information due to they generally adopt an uniform template mesh. To this end, this paper proposes a novel hypergraph-based human mesh hierarchical representation that enables the expression of vertices at body, parts, and vertices perspectives, corresponding to global, local and individual, respectively. And then a novel HyperGraph Attention-based human mesh reconstruction network (HGaMRNet) is put forward in turn, which mainly consists of two modules and can efficiently capture human information from different granularities of the mesh. Specifically, the first module, Body2Parts, decouples a body into local parts, and leverages Mix-Attention (MAT) based feature encoder to learn visual cues and semantic information of the parts for capturing complex human kinematic relationships from monocular images. The second module, Part2Vertices, transfers part features to the corresponding vertices through an adaptive incidence matrix, and utilizes a HyperGraph Attention network to update the vertex features. This is conductive to learning the fine-grained morphological information of a human mesh. All in one, supported by the hypergraph-based hierarchical representation of the human mesh, HGaMRNet balances the effects of neighbor vertex from different levels properly and eventually promotes the reconstruction accuracy of human mesh. Experiments conducted on both Human3.6M and 3DPW datasets show that HGaMRNet outperforms most of the existing image-based human mesh reconstruction methods.

Prediction of the 3D shape of the L1 vertebral body from adjacent vertebrae.

The aim of treatments of vertebral fractures is the anatomical reduction to restore the physiological biomechanics of the spine and the stabilization of the fracture to allow bone healing. However, the three-dimensional shape of the fractured vertebral body before the fracture is unknown in the clinical setting. Information about the pre-fracture vertebral body shape could help surgeons to select the optimal treatment. The goal of this study was to develop and validate a method based on Singular Value Decomposition (SVD) to predict the shape of the vertebral body of L1 from the shapes of T12 and L2. The geometry of the vertebral bodies of T12, L1 and L2 vertebrae of 40 patients were extracted from CT scans available from the VerSe2020 open-access dataset. Surface triangular meshes of each vertebra were morphed onto a template mesh. The set of vectors with the node coordinates of the morphed T12, L1 and L2 were compressed with SVD and used to build a system of linear equations. This system was used to solve a minimization problem and to reconstruct the shape of L1. A leave-one-out cross-validation was performed. Moreover, the approach was tested against an independent dataset with large osteophytes. The results of the study show a good prediction of the shape of the vertebral body of L1 from the shapes of the two adjacent vertebrae (mean error equal to 0.51±0.11mm on average, Hausdorff distance equal to 2.11±0.56mm on average), compared to current CT resolution typically used in the operating room. The error was slightly higher for patients presenting large osteophytes or severe bone degeneration (mean error equal to 0.65±0.10mm, Hausdorff distance equal to 3.54±1.03mm). The accuracy of the prediction was significantly better than approximating the shape of the vertebral body of L1 by the shape of T12 or L2. This approach could be used in the future to improve the pre-planning of spine surgeries to treat vertebral fractures.

Left atrium surface mesh reconstruction from cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: None. Background The strive towards personalised medicine is universal across all aspects of healthcare. In the management of atrial fibrillation (AF), this can take the form of shape analysis tools to predict an individual’s outcome from catheter ablation (1–3), or electrophysiological simulations to plan an ablation strategy (4–6). Fundamental to their application is our ability to derive accurate three-dimensional (3D) anatomical models of a patient’s left atrial geometry from cross-sectional imaging. MRI is widely utilised in this regard due to its excellent tissue characterisation. However, constructing surface meshes can be challenging given its relatively sparse two-dimensional (2D) image planes. A particular challenge exists in producing a mesh that balances concordance with left atrium segmentation and smoothing terms to eliminate sharp edges and artifact from the contouring process. Objective Here, we describe a novel method for left atrium surface mesh reconstruction from MRI contours. Methods Cardiac MRI was performed on ten patients with paroxysmal or persistent AF, on a 3T clinical MR scanner using phased-array receiver coils. Image sequences included respiratory and ECG-gated 15-minute post contrast late gadolinium enhancement, with a 1.3mm slice thickness and in-plane resolution of 0.66×0.66mm2. Seven individuals were in sinus rhythm, and three in AF at the time of MRI. A single, custom, graphical interface tool implemented in Matlab R2014a enabled 3D mesh reconstruction from 2D MRI slices (7,8). Endocardial surfaces of the left atrium were manually segmented by an experienced clinician, with the pulmonary veins and left atrial appendage excluded at their ostia (Figure 1A). Our tool produced an initial sparse representation with vertices equally spaced along the contours (Figure 1B). Attractor points were defined along the contours, with subdivision and Laplacian smoothing applied to the mesh. This was balanced with a deformation step, which iteratively pulled the mesh towards the attractor points (Figure 1C), resulting in its final shape (Figure 1D). Results Reconstruction performance was evaluated by measuring the distance between heart contours and the reconstructed 3D surface for each case, producing the value for average misalignment 0.71 ± 0.58mm, which compares favourably to the in-plane resolution. Conclusions Our approach offers a highly accurate method for producing 3D anatomical models of the left atrium from MRI, which is fully automated following segmentation through an interactive graphical interface. The iterative optimisation between attractor-based deformation towards the original contours and the Laplacian smoothing enables us to create smooth geometries which remain true to their segmentations. Furthermore, our approach does not require the use of a template mesh, which can result in final geometries being biased towards the template.

Using Excimer Laser for Manufacturing Stimuli Responsive Membranes.

A 248 nm KrF excimer laser can be used to manufacture temperature and pH-responsive polymer-based membranes for controlled transport applications. This is done by a two-step approach. In the first step, well-defined/shaped and orderly pores are created on commercially available polymer films by ablation by using an excimer laser. The same laser is used subsequently for energetic grafting and polymerization of a responsive hydrogel polymer inside the pores fabricated during the first step. Thus, these smart membranes allow controllable solute transport. In this paper, determination of appropriate laser parameters and grafting solution characteristics are illustrated to obtain the desired membrane performance. Fabrication of membranes with 600 nm to 25 μm pore sizes by using the laser through different metal mesh templates is discussed first. Laser fluence and the number of pulses need to be optimized to obtain the desired pore size. Mesh size and film thickness primarily control the pore sizes. Typically, pore size increases with increasing fluence and the number of pulses. Larger pores can be created by using higher fluence at a given laser energy. The vertical cross-section of the pores turns out to be inherently tapered due to the ablative action of the laser beam. The pores created by laser ablation can be grafted with PNIPAM hydrogel by using the same laser to perform a bottom-up grafting-from type pulsed laser polymerization (PLP) in order to achieve the desired transport function controlled by temperature. For this purpose, a set of laser frequencies and pulse numbers need to be determined to obtain the desired hydrogel grafting density and the extent of cross-linking, which ultimately provide controlled transport by smart gating. In other words, on-demand switchable solute release rates can be achieved by controlling the cross-linking level of the microporous PNIPAM network. The PLP process is extremely fast (few seconds) and provides higher water permeability above the lower critical solution temperature (LCST) of the hydrogel. Experiments have shown high mechanical integrity for these pore-filled membranes, which can sustain pressures up to 0.31 MPa. The monomer (NIPAM) and cross-linker (mBAAm) concentrations in the grafting solution need to be optimized in order to control the network growth inside the support membrane pores. The cross-linker concentration typically has a stronger effect on the temperature responsiveness. The pulsed laser polymerization process described can be extended to different unsaturated monomers, which can be polymerized by the free radical process. For example, poly(acrylic acid) can be the grafted to provide pH responsiveness to membranes. As for the effects of thickness, a decreasing trend is observed in the permeability coefficient with increasing thickness. Furthermore, the film thickness has little or no effect on PLP kinetics. The experimental results have shown that membranes manufactured by excimer laser are excellent choices for applications where flow uniformity is the prime requirement, as they possess uniform pore sizes and distribution.

A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors

Abstract Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.