Implicit Surface Model Research Articles

Implicit Neural Representations With Structured Latent Codes for Human Body Modeling.

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Furthermore, we combine Neural Body with implicit surface models to improve the learned geometry. To evaluate our approach, we perform experiments on both synthetic and real-world data, which show that our approach outperforms prior works by a large margin on novel view synthesis and 3D reconstruction. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

Design the femoral implant matched anatomical Ti64 implant and compare among lattice structures topology optimization and cylindrical pores

Trabecular bone, a porous and spongy kind of bone tissue composed of a lattice of connecting rods and/or plates, is crucial for the transfer of weight in crucial joints including the spine, hip, and knee. It is impacted by a variety of metabolic bone illnesses, such as osteoporosis, which result in bone loss, reduced bone strength, and an elevated risk of bone fracture. To promote osseointegration due to the permeable porous, we suggest, in this study, a new approach to design a femoral permeable porous implant to achieve suitable stiffness, lightweight, and biocompatibility for the bone. Accordingly, data are collected from a digital bio-model to design a femoral implant for mimics segmental bone defect (SBD) with a biocompatible material Ti6Al4V (Ti64). To do this, three lattice structures (LSs) are compared and derived from implicit surface modeling called a triply periodic minimal surface (TPMS) to obtain the LS suitable to create it within the solid inner volume of the tibia implant. Thus, performed the finite element analysis (FEA) method for the enhanced Designs and compare them in terms of stress, deformation, and lightweight.

Online Distance Field Priors for Gaussian Process Implicit Surfaces

Gaussian process (GP) implicit surface models provide environment and object representations which elegantly address noise and uncertainty while remaining sufficiently flexible to capture complex geometry. However, GP models quickly become intractable as the size of the observation set grows—a trait which is difficult to reconcile with the rate at which modern range sensors produce data. Furthermore, naïve applications of GPs to implicit surface models allocate model resources uniformly, thus using precious resources to capture simple geometry. In contrast to prior work addressing these challenges though model sparsification, spatial partitioning, or ad-hoc filtering, we propose introducing model bias online through the GP’s mean function. We achieve more accurate distance fields using smaller models by creating a distance field prior from features which are easy to extract and have analytic distance fields. In particular, we demonstrate this approach using linear features. We show the proposed distance field halves model size in a 2D mapping task using data from a SICK S300 sensor. When applied to a single 3D scene from the TUM RGB-D SLAM dataset, we achieve a fivefold reduction in model size. Our proposed prior results in more accurate GP implicit surfaces, while allowing existing models to function in larger environments or with larger spatial partitions due to reduced model size.

Geometric models for plant leaf area estimation from 3D point clouds: A comparative study

Measuring leaf area is a critical task in plant biology. Meshing techniques, parametric surface modelling and implicit surface modelling allow estimating plant leaf area from acquired 3D point clouds. However, there is currently no consensus on the best approach because of little comparative evaluation. In this paper, we provide evidence about the performance of each approach, through a comparative study of four meshing, three parametric modelling and one implicit modelling methods. All selected methods are freely available and easy to use. We have also performed a parameter sensitivity analysis for each method in order to optimise its results and fully automate its use. We identified nine criteria affecting the robustness of the studied methods. These criteria are related to either the leaf shape (length/width ratio, curviness, concavity) or the acquisition process (e.g. sampling density, noise, misalignment, holes). We used synthetic data to quantitatively evaluate the robustness of the selected approaches with respect to each criterion. In addition we evaluated the results of these approaches on five tree and crop datasets acquired with laser scanners or photogrammetry. This study allows us to highlight the benefits and drawbacks of each method and evaluate its appropriateness in a given scenario. Our main conclusion is that fitting a Bézier surface is the most robust and accurate approach to estimate plant leaf area in most cases.

Peptide adsorption on silica surfaces: Simulation and experimental insights

The understanding of interactions between proteins with silica surface is crucial for a wide range of different applications: from medical devices, drug delivery and bioelectronics to biotechnology and downstream processing. We show the application of EISM (Effective Implicit Surface Model) for discovering the set of peptide interactions with silica surface. The EISM is employed for a high-speed computational screening of peptides to model the binding affinity of small peptides to silica surfaces. The simulations are complemented with experimental data of peptides with silica nanoparticles from microscale thermophoresis and from infrared spectroscopy. The experimental work shows excellent agreement with computational results and verifies the EISM model for the prediction of peptide-surface interactions. 57 peptides, with amino acids favorable for adsorption on Silica surface, are screened by EISM model for obtaining results, which are worth to be considered as a guidance for future experimental and theoretical works. This model can be used as a broad platform for multiple challenges at surfaces which can be applied for multiple surfaces and biomolecules beyond silica and peptides.

Design of three-dimensional Voronoi strut midsoles driven by plantar pressure distribution

Abstract The customized production pattern has brought significant innovation to the design and manufacturing of footwear. To improve the matching degree between the consumer’s feet and deepen the customization of the sole’s personalized function, a three-dimensional (3D) Voronoi strut midsole structural design method driven by plantar pressure distribution is proposed in this paper, which not only realizes the functional requirements but also takes into account the aesthetic of midsoles. In this method, the foot characteristics and pressure information obtained by the foot measuring system are employed as the data-driven basic of the midsole structural design, and a weighted random sampling strategy is introduced for constructing the Voronoi sites. Moreover, a Voronoi clipping algorithm is proposed to make the 3D Voronoi diagram adaptive to the midsole boundary. And then, taking the clipped 3D Voronoi edges as skeleton lines, the smooth and continuous 3D Voronoi strut midsoles are generated by the implicit surface modelling technology and implicit function fusion. All the algorithms are integrated into a digital framework by independent programming. And both the static and dynamic tests show that the 3D Voronoi strut midsole can make the plantar pressure distribution more homogenous and can effectively reduce the load on the metatarsal and heel region. What is more, it can provide superior energy absorption and cushioning properties, offer better resilience, bring consumers a more comfortable wearing experience and reduce the probability of joint injury caused by the abnormal plantar pressure concentration.

Modelling peptide adsorption energies on gold surfaces with an effective implicit solvent and surface model

The interaction of proteins and peptides with inorganic surfaces is relevant in a wide array of technological applications. A rational approach to design peptides for specific surfaces would build on amino-acid and surface specific interaction models, which are difficult to characterize experimentally or by modeling. Even with such a model at hand, the large number of possible sequences and the large conformation space of peptides make comparative simulations challenging. Here we present a computational protocol, the effective implicit surface model (EISM), for efficient in silico evaluation of the binding affinity trends of peptides on parameterized surface, with a specific application to the widely studied gold surface. In EISM the peptide surface interactions are modeled with an amino-acid and surface specific implicit solvent model, which permits rapid exploration of the peptide conformational degrees of freedom. We demonstrate the parametrization of the model and compare the results with all-atom simulations and experimental results for specific peptides.

An image segmentation approach on improved implicit surface model in straddle strategy

At present, some experts and scholars do not explicitly mention the static model in the analysis of the straddle strategy, which leads to the research results are not ideal. This paper unifies the static model and proposes a method to analyze the arbitrage opportunities in the bond market. At the same time, this paper combines image processing technology to analyze image method and improve image clarity. In addition, this paper uses the contrast method to carry out traditional static model analysis, combines the research requirements to improve the model, obtains the research data through network data collection, and constructs the model for experimental data analysis. Finally, the paper processes the model data through image processing to obtain identifiable statistical images. The research shows that the model proposed in this study has certain validity and can provide theoretical reference for subsequent related research.

Implicit Surface Modelling and Parametric Optimization for Self-cleaning Portable Toilets Using a Rotary Jet Head Cleaning in Grasshopper

Implicit Surface Modelling and Parametric Optimization for Self-cleaning Portable Toilets Using a Rotary Jet Head Cleaning in Grasshopper

Multifidelity Sampling for Fast Bayesian Shape Estimation With Tactile Exploration

This article presents a novel multifidelity-based optimal sampling method to rapidly estimate the shape of an object from the touch-down points on its surface given a highly limited number of sampling trials. The proposed approach attempts to improve the existing shape estimation via tactile exploration, which uses Gaussian process regression for implicit surface modeling with sequential sampling. The main objective is to make the process of sample point selection more efficient and systematic such that the unknown shape can be estimated fast and accurately with highly limited sample points (e.g., less than 1% of the original dataset). Specifically, we propose to select the next best sample point based on two optimization criteria: 1) the mutual information (MI) for uncertainty reduction, and 2) the local curvature for fidelity enhancement. The combination of these two objectives leads to an optimal sampling process that balances between the exploration of the whole shape and the exploitation of the local area where the higher fidelity (or more sampling) is required. Simulation and experimental results successfully demonstrate the advantage of the proposed method in terms of estimation speed and accuracy over the conventional methods. Our approach allows us to reconstruct recognizable three dimensional shapes using only around optimally selected 0.4% of the original dataset.

Rational Design of Iron Oxide Binding Peptide Tags.

Owing to their extraordinary magnetic properties and low-cost production, iron oxide nanoparticles (IONs) are in the focus of research. In order to better understand interactions of IONs with biomolecules, a tool for the prediction of the propensity of different peptides to interact with IONs is of great value. We present an effective implicit surface model (EISM), which includes several interaction models. Electrostatic interactions, van der Waals interactions, and entropic effects are considered for the theoretical calculations. However, the most important parameter, a surface accessible area force field contribution term, derives directly from experimental results on the interactions of IONs and peptides. Data from binding experiments of ION agglomerates to different peptides immobilized on cellulose membranes have been used to parameterize the model. The work was carried out under defined environmental conditions; hence, effects because of changes, for example structure or solubility by changing the surroundings, are not included. EISM enables researchers to predict the binding of peptides to IONs, which we then verify with further peptide array experiments in an iterative optimization process also presented here. Negatively charged peptides were identified as best binders for IONs in Tris buffer. Furthermore, we investigated the constitution of peptides and how the amount and position of several amino acid side chains affect peptide-binding. The incorporation of glycine leads to higher binding scores compared to the incorporation of cysteine in negatively charged peptides.

Porous Scaffold Design Based on Minimal Surfaces: Development and Assessment of Variable Architectures

In tissue engineering, biocompatible porous scaffolds that try to mimic the features and function of the bone are of great relevance. In this paper, an effective method for the design of 3D porous scaffolds is applied to the modelling of structures with variable architectures. These structures are of interest since they are more similar to the stochastic configuration of real bone with respect to architectures made of a unit cell replicated in three orthogonal directions, which are usually considered for this kind of applications. This property configures them as, potentially, more suitable to satisfy simultaneously the biological requirements and those relative to the mechanical strength. The procedure implemented is based on the implicit surface modelling method and the use of a triply periodic minimal surface (TPMS), specifically, the Schwarz’s Primitive (P) minimal surface, whose geometry was considered for the development of scaffolds with different configurations. The representative structures modelled were numerically analysed by means of finite element analysis (FEA), considering them made of a biocompatible titanium alloy. The architectures considered were thus assessed in terms of the relationship between the geometrical configuration and the mechanical response to compression loading.

Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface.

We propose a novel approach to predict the Abdominal Aortic Aneurysm(AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way. We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time. A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach. We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.

Tunneling Reaction Kinetics for the Hydrogen Abstraction Reaction H + H2S → H2 + HS in the Interstellar Medium.

The hydrogen abstraction reaction between H and H2S, yielding HS and H2 as products, has been studied within the framework of interstellar surface chemistry. High-temperature rate constants below 2000 K are calculated in the gas phase and are in agreement with previously reported values. Subsequently, low-temperature rate constants down to 55 K are presented for the first time that are of interest to astrochemistry, i.e., covering both bimolecular and unimolecular reaction mechanisms. For this, a so-called implicit surface model is used. In strict terms, this is a structural gas-phase model in which the restriction of the rotation in the solid state is taken into account. The calculated kinetic isotope effects are explained in terms of the difference in activation and delocalization. All rate constants are calculated at the UCCSD(T)-F12/cc-VTZ-F12 level of theory. Finally, we show that the energetics of the reaction is affected to an only small extent by the presence of H2O or H2S molecular clusters that simulate an ice surface, calculated at the MPWB1K/def2-TZVP level of theory.

Modeling of gas diffusion layers with curved fibers using a genetic algorithm

This paper presents a methodology for modeling microstructures of fibrous porous media with curved fibers. The developed methodology utilizes implicit periodic surface model coupled with the genetic algorithm (GA) optimization to construct the porous microstructures. The fibers profile is represented by the periodic implicit surfaces and their orientation and curvature are determined by GA optimization. To reconstruct the microstructures with higher resemblance to the actual porous media GA is utilized to minimize the fibers stored strain energy and their intersection volumes. Coupling the image processing techniques to the geometry construction procedure the morphological and transport properties of the constructed microstructures are also determined. To verify the feasibility and the accuracy of the proposed methodology the microstructure of Freudenberg H2315 GDL is constructed and characterized. The presented methodology enables a parametric design approach. Thus, the effects of the microstructure's properties e.g., fibers diameter, fibers orientation and porosity of the porous structure on the transport properties of the fibrous media are investigated.

Active tactile exploration with uncertainty and travel cost for fast shape estimation of unknown objects

In this paper, active tactile exploration for object shape estimation is explored. A prior work suggested to touch the most uncertain part of the estimated shape for minimizing the required number of touches. In this paper, it is pointed out that it may not be the best approach for fast estimation. We propose a novel criterion in active touch point selection for fast estimation, which considers both uncertainty of shape estimation and travel cost to touch. Our method employs a Gaussian process implicit surface model to learn the object shape from tactile information, which allows us to evaluate the uncertainty of the shape estimation with an analytic form. To estimate the travel costs for all the touch candidates, our method utilizes a computationally-efficient graph-based path planning method based on stochastic optimal control theory. Simulations with 2D and 3D objects and real-robot experiments with a 7DOF robot arm and a single finger device equipped with a tactile sensor are conducted. Experimental results verify the effectiveness of our method for fast tactile object shape estimation as compared to passive exploration and active exploration that touches the most uncertain part of the estimated shape.

Active multi-scale modeling and gas permeability study of porous metal fiber sintered felt for proton exchange membrane fuel cells

Porous metal fiber sintered felt (PMFSF) is a promising critical component in proton exchange membrane fuel cells, having the ability of simultaneously acting as the flow field plate, gas diffusion layers and also the catalyst layers support, and owing the property of multi-scale surface morphology. A simple multi-scale mathematical method was proposed to actively construct three dimensional models of PMFSF’s microstructure by synthesizing the implicit periodic surface (PS) model and the Weierstrass-Mandelbrot (W-M) fractal geometry. In this method, the PS model described the macro overall fiber shape, and the W-M fractal geometry modeled the micro fractal roughness topography attached. Based on the method, multi-scale fractal PMFSF models were reconstructed according to morphology parameters of physical PMFSFs, and were discretized in ANSYS/ICEM to generate refined mesh for computational fluid dynamics analysis. To verify the validity of the proposed modeling approach, PMFSFs with different porosity and fiber orientation are generated, and then the effects of the fractal surface topography and the fractal parameters such as fractal dimension and height scaling parameter on the gas permeability of PMFSF were investigated. The numeric simulation results show that the influence of the fractal topography on the in-plane and through-plane permeability of PMFSF cannot be ignored, and the permeability of fractal PMFSF models agrees with experimental measurements better. Especially, the results imply that the fractal morphology may have the potential to adjust the anisotropic properties of PMFSFs’ permeability. It is further found that the larger the fractal dimension is and the lower the height scaling parameter is, the better the permeability of PMFSF will be. The synthesis approach and numerical simulation method may facilitate the development of active functional design mode to predict and optimize key characteristics of PMFSF ahead of manufacture.

Implicit Surface Modeling from Imprecise Point Clouds

Abstract. In applying optical methods for automated 3D indoor modelling, the 3D reconstruction of objects and surfaces is very sensitive to both lighting conditions and the observed surface properties, which ultimately compromise the utility of the acquired 3D point clouds. This paper presents a robust scene reconstruction method which is predicated upon the observation that most objects contain only a small set of primitives. The approach combines sparse approximation techniques from the compressive sensing domain with surface rendering approaches from computer graphics. The amalgamation of these techniques allows a scene to be represented by a small set of geometric primitives and to generate perceptually appealing results. The resulting scene surface models are defined as implicit functions and may be processed using conventional rendering algorithms such as marching cubes, to deliver polygonal models of arbitrary resolution. It will also be shown that 3D point clouds with outliers, strong noise and varying sampling density can be reliably processed without manual intervention.

Adequate inner bound for geometric modeling with compact field functions

Recent advances in implicit surface modeling now provide highly controllable blending effects. These effects rely on the field functions of R3→R in which the implicit surfaces are defined. In these fields, there is an outside part in which blending is defined and an inside part. The implicit surface is the interface between these two parts. As recent operators often focus on blending, most efforts have been made on the outer part of field functions and little attention has been paid on the inner part. Yet, the inner fields are important as soon as difference and intersection operators are used. This makes its quality as crucial as the quality of the outside. In this paper, we analyze these shortcomings, and deduce new constraints on field functions such that differences and intersections can be seamlessly applied without introducing discontinuities or field distortions. In particular, we show how to adapt state of the art gradient-based union and blending operators to our new constraints. Our approach enables a precise control of the shape of both the inner or outer field boundaries. We also introduce a new set of asymmetric operators tailored for the modeling of fine details while preserving the integrity of the resulting fields.

Mathematical Methods for Images and Surfaces 2011

Mathematical Methods for Images and Surfaces 2011