Shape Estimation Research Articles

LEAPSE: Learning Environment Affordances for 3D Human Pose and Shape Estimation.

We live in a 3D world where people interact with each other in the environment. Learning 3D posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner. Existing methods mostly ignore the environment and estimate the human body independently from the surroundings. We capture the influences of non-contact and contact instances on a posed body as an adequate representation of the "environment affordances". The proposed method learns the global relationships between 3D joints, body mesh vertices, and salient instances as environment affordances on the human body. LEAPSE achieved state-of-the-art results on the 3DPW dataset with many affordance instances, and also demonstrated excellent performance on Human3.6M dataset. We further demonstrate the benefit of our method by showing that the performance of existing weak models can be significantly improved when combined with our environment affordance module.

Reliability evaluation for Weibull distribution with heavily Type II censored data

AbstractThe lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods.

SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation.

Existing Transformers for monocular 3D human shape and pose estimation typically have a quadratic computation and memory complexity with respect to the feature length, which hinders the exploitation of fine-grained information in high-resolution features that is beneficial for accurate reconstruction. In this work, we propose an SMPL-based Transformer framework (SMPLer) to address this issue. SMPLer incorporates two key ingredients: a decoupled attention operation and an SMPL-based target representation, which allow effective utilization of high-resolution features in the Transformer. In addition, based on these two designs, we also introduce several novel modules including a multi-scale attention and a joint-aware attention to further boost the reconstruction performance. Extensive experiments demonstrate the effectiveness of SMPLer against existing 3D human shape and pose estimation methods both quantitatively and qualitatively. Notably, the proposed algorithm achieves an MPJPE of 45.2mm on the Human3.6M dataset, improving upon the state-of-the-art approach (Lin et al., 2021) by more than 10% with fewer than one-third of the parameters.

Adequate laser focusing and signal acquisition conditions for 3D measurement of electric-field distribution by the E-FISHG method

Electric field measurement using electric-field-induced second-harmonic generation (E-FISHG) has attracted attention because of its non-invasiveness and high spatiotemporal resolution. In the electric field measurement by the E-FISHG method, the applied electric-field profile along the laser path outside the focal spot affects the second-harmonic generation (SHG) signal. We have proposed a method of calibrating and inferring the applied electric-field profile from the SHG distribution along the laser path. In our previous research, the successful inference of a relatively simple electric-field profile from a series of SHG signals was demonstrated. To measure more complex electric-field profiles, we apply our method to three cases of electric-field profiles: (1) the profile with different sharpness, (2) the profile with two peaks, and (3) the profile with noise superimposed on the SHG signal. The applied electric-field distribution can be inferred within 10% error by adequately choosing the confocal parameter. We also provide guidelines for the required signal acquisition region and measurement pitch when the approximate shape of the applied electric field is known, which are important for actual measurement.

Freeform Shape Fabrication by Kerfing Stiff Materials

AbstractFast, flexible, and cost efficient production of 3D models from 2D material sheets is a key component in digital fabrication and prototyping. In order to achieve high quality approximations of freeform shapes, a common set of methods aim to produce bendable 2D cutouts that are then assembled. So far bent surfaces are achieved automatically by computing developable patches of the input surface, e.g. in the context of papercraft. For stiff materials such as medium‐density fibreboard (MDF) or plywood, the 2D cutouts require the application of additional cutting patterns (“kerfing”) to make them bendable. Such kerf patterns are commonly constructed with considerable user input, e.g. in architectural design. We propose a fully automatic method that produces kerfed cutouts suitable for the assembly of freeform shapes from stiff material sheets. By exploring the degrees of freedom emerging from the choice of bending directions, the creation of box joints at the patch boundaries as well as the application of kerf cuts with adaptive density, our method is able to achieve a high quality approximation of the input.

Shape estimation for a TPU-based multi-material 3D printed soft pneumatic actuator using deep learning models

Shape estimation for a TPU-based multi-material 3D printed soft pneumatic actuator using deep learning models

Three-dimensional shape estimation of wires from three-dimensional X-ray computed tomography images of electrical cables

Three-dimensional shape estimation of wires from three-dimensional X-ray computed tomography images of electrical cables

Seamount detection using SWOT-derived vertical gravity gradient: advancements and challenges

SUMMARY Launched on 2022 December 16, the Surface Water and Ocean Topography (SWOT) satellite, using synthetic aperture radar interferometric techniques, measures sea surface heights (SSHs) across two 50-km-wide swaths, offering high-resolution and accurate 2-D SSH observations. This study explores the efficiency of SWOT in seamount detection employing the vertical gravity gradient (VGG) derived from simulated SWOT SSH data. Simulated circular and elliptical seamounts (height: 900–1500 m) are integrated within the South China Sea's 4000 m background depths. Geoid perturbations induced by these seamounts are extracted through the residual depth model principle, subsequently merged with the DTU21MSS model for simulating SWOT SSH observations. For comparative assessment, SSH data from Jason-2 and Cryosat-2 are included. An automatic algorithm (AIFS) is presented to identify seamount centres and base polygons using VGG derived from simulated altimeter SSH data. The analysis reveals SWOT-derived VGGs precisely locate all seamount centres, base polygons and elliptical seamount azimuths. The merged Jason-2 and Cryosat-2 data face challenges with identifying small circular and elliptical seamounts. Detecting long narrow elliptical seamounts remains arduous; however, SWOT-derived VGGs successfully elucidate the approximate shapes and major axis azimuths of the elliptical seamounts. Validated against ‘true values’ of VGG, the root-mean-squared deviation (RMSD) of SWOT-derived VGG stands at 1.33 Eötvös, whereas the merged Jason-2 and Cryosat-2 data exhibit an RMSD of 1.93 Eötvös. This study shows the enhanced capability of SWOT from its high-resolution 2-D SSH observations in advancing seamount detection via satellite-derived VGG. We identify challenges and recommend improved detections using data integration and machine learning.

Drive-by bridge mode shape identification using novel reference-based component scaling method

This article proposes a novel method, entitled Reference-based Component Scaling (RCS) method, for estimation of bridge mode shapes using vehicle scanning methods. The proposed method uses two instrumented vehicles to measure the accelerations on the bridge. While one vehicle remains stationary on a pre-determined location, the other one travels over the bridge with a constant speed both exciting the bridge and recording its response. The accelerations recorded on both vehicles are then processed using variational mode decomposition method and Hilbert transform to obtain their modal components. Leveraging its comprehensive framework that incorporates modal components derived from both vehicles, the RCS method effectively tackles the primary challenges encountered by vehicle scanning methods. These challenges include: i) shifting and shrinking effects of bridge damping on mode shapes, ii) distortions in modal components due to incomplete elimination of the roughness effect and iii) distortions in modal components caused by acceleration peaks during the vehicle´s transition from a rigid platform to the bridge. To this end, the RCS method addresses adverse effects of bridge damping without a priori knowledge of the modal damping ratios. To assess the accuracy of the proposed method, numerical analyses were carried out on three different bridges with different number of spans and boundary conditions. The effect of crucial parameters such as road roughness, bridge damping, uncertainties in measurements, and existing traffic on the accuracy of the proposed method was investigated. The results of the numerical study show that the RCS method stands alone in providing accurate estimates of mode shapes of bridges that are seated on elastic bearings, that have realistic damping ratios and relatively high road roughness profiles.

(Doing) belonging as technology of power: how the principle of ‘gender equality’ governs membership in Swiss society

ABSTRACT This paper analyses how the principle of gender equality informs politics of belonging in Switzerland. We propose to conceptualize ‘doing belonging’ as a technology of power and we examine how actors in (non-)institutional settings employ it as part of professional and personal action. The paper draws on two case studies: an ethnography of institutions in charge of Swiss naturalization procedures and a series of qualitative interviews with migrant descendants. It unpacks how individuals negotiate belonging in different social contexts that are marked by specific power relations. First, we reveal how ideas of gender equality shape the implementation of state policies in naturalization procedures by selectively assessing the candidates according to their national and assumed cultural background. Second, we show how naturalized individuals are doing belonging when confronting external ascriptions as being ‘gender unequal’. The analysis contributes to a better understanding of the role the principle of gender equality plays in politics of belonging enacted at a micro-sociological and individual level, thus illuminating the gendered underpinnings of migration politics.

Toward Improved Simulations of Disruptive Reservoirs in Global Hydrological Modeling

AbstractAccurate simulation of reservoirs has been a challenge for global hydrological models due to highly discontinuous water management and uncertainties in reservoir shape representation. In addition, at a global scale, it is crucial to consider those reservoirs that disrupt the downstream flow regime. We augment the mesoscale Hydrological Model with a newly developed lake module (LM) that incorporates an existing reservoir regulation scheme with non‐consumptive demand predictions from random forest. We also evaluate the sensitivity of reservoir shape on streamflow and evaporation for three shape approximations of varying complexities. We tested the LM across 31 non‐consumptive reservoirs covering an extensive range of hydroclimatic characteristics and demonstrate the applicability by using freely available global reservoir information. Streamflow simulations with reservoirs and model calibration show a median Kling‐Gupta Efficiency improvement of +0.94 (calibration) and +0.77 (validation) when compared against model simulations without reservoirs and default parameter set. We find reservoir evaporation highly sensitive to reservoir shape with half‐pyramid approximation consistently resulting in best fit at reservoirs with surveyed bathymetry. In contrast, the linear approximation (rectangular prism) produced a median bias of +114% relative to half‐pyramid, for estimating evaporation, across all the reservoirs. Streamflow simulations were insensitive to the reservoir shape. Our analysis shows that 30% of the non‐consumptive hydropower reservoirs of the GRanD data set are non‐disruptive and can be excluded without loss to model realism. Further work is necessary for testing the regulation approach in reservoirs with consumptive water usage.

Shape Sensing for Continuum Robotics Using Optoelectronic Sensors with Convex Reflectors

Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as minimally invasive surgery, engine repairs and search and rescue operations, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing techniques, for example, Fibre Bragg grating (FBG) technology, inertial measurement unit (IMU) sensor networks, or stretch sensors. Previously, an optics-based method using optoelectronic sensors was explored, offering a simple and cost-effective solution for shape sensing in a flexible tendon-actuated manipulator in two orientations. This was based on proximity-modulated angle estimation and has been the basis for the shape sensing method addressed in this paper. The improved and miniaturised technique demonstrated in this paper is based on the use of a convex shaped reflector with optoelectronic sensors integrated into a tendon-actuated robotic manipulator. Upgraded sensing capability is achieved using optimisation of the convex reflector shape in terms of sensor range and resolution, and improved calibration is achieved through the integration of spherical bearings for friction-free motion. Shape estimation is achieved in two orientations upon calibration of sensors, with a maximum Root-Mean-Square Error (RMS) of 3.37°.

Statistical inference for multivariate extremes via a geometric approach

Abstract A geometric representation for multivariate extremes, based on the shapes of scaled sample clouds in light-tailed margins and their so-called limit sets, has recently been shown to connect several existing extremal dependence concepts. However, these results are purely probabilistic, and the geometric approach itself has not been fully exploited for statistical inference. We outline a method for parametric estimation of the limit set shape, which includes a useful non-/semi-parametric estimate as a pre-processing step. More fundamentally, our approach provides a new class of asymptotically motivated statistical models for the tails of multivariate distributions, and such models can accommodate any combination of simultaneous or non-simultaneous extremes through appropriate parametric forms for the limit set shape. Extrapolation further into the tail of the distribution is possible via simulation from the fitted model. A simulation study confirms that our methodology is very competitive with existing approaches and can successfully allow estimation of small probabilities in regions where other methods struggle. We apply the methodology to two environmental datasets, with diagnostics demonstrating a good fit.

Cold Emulsified Nanoemulsion: Enhancing Delivery and Stability of Thermolabile Acyclovir

Acyclovir is an acyclic purine nucleoside widely used as an antiviral agent with good topical efficacy. Acyclovir is a thermolabile drug, making it challenging to formulate a stable and effective emulsion via a conventional emulsification process. This study aims to develop, optimize, and characterize the nanoemulsion of thermolabile drug acyclovir, manufactured by adopting a cold emulsification process and compare the physicochemical properties and diffusion pattern with a leading marketed formulation. Diffusion studies were conducted using Franz diffusion cells. The optimized formulation was subjected to quantitative estimation of acyclovir, impurity profiling, viscosity, pH, vesicle size, shape, and polydispersibility index (PDI). In the current study, the particle size of the nanoemulsion varies from 66.25 to 244.40 nm, and the zeta potential revealed a high negative surface charge on the particles. Transmission electron microscopy images revealed spherical-shaped, non-aggregated, and discrete globules. The in-vitro diffusion study of acyclovir nanoemulsion showed enhanced penetration efficacy than the conventional emulsion. All these observations signified that the cold emulsification process for manufacturing nanoemulsion is appropriate for thermolabile and pH-sensitive drugs. The study demonstrates enhanced stability and efficacy of optimized acyclovir nanoemulsion through cold emulsification.

Monocular 3D Hand Mesh Recovery via Dual Noise Estimation

Current parametric models have made notable progress in 3D hand pose and shape estimation. However, due to the fixed hand topology and complex hand poses, current models are hard to generate meshes that are aligned with the image well. To tackle this issue, we introduce a dual noise estimation method in this paper. Given a single-view image as input, we first adopt a baseline parametric regressor to obtain the coarse hand meshes. We assume the mesh vertices and their image-plane projections are noisy, and can be associated in a unified probabilistic model. We then learn the distributions of noise to refine mesh vertices and their projections. The refined vertices are further utilized to refine camera parameters in a closed-form manner. Consequently, our method obtains well-aligned and high-quality 3D hand meshes. Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10% but also achieves state-of-the-art performance. Project page: https://github.com/hanhuili/DNE4Hand.

Study on Properties of Glass-Fiber-Fabric-Reinforced Microwave-Absorbing Composites.

In this paper, the glass-fiber-fabric-reinforced resin-based absorbing composites were prepared, and their microwave-absorbing properties were studied via simulation and experiment. The simulation results show that the absorption bandwidth of the absorbing material can cover the X\C\S band, respectively, at different thicknesses. The minimum reflection loss (RL) of the composite with a thickness of 2.2 mm is -27.4 dB at 5.95 GHz. However, the experiment results are quite different from those of the simulation. The metallographic results indicate that it is the change of the mass fraction of the absorbents in the composites after curing that causes the difference. According to the metallographic results, three shape approximation methods were proposed to calculate the real mass ratio of the absorbents in the composites, namely, parallelogram approximation, bows approximation, and elliptical approximation. Meanwhile, the structural parameter Kf was introduced to optimize the calculation results. The electromagnetic parameters of the material based on the calculation results were measured, and the results show that the simulation results obtained via bow approximation have a better coincidence to the experiment results, and the mass ratio of the absorbent raises by around 9.95%, which lays a foundation for the subsequent design of microwave-absorbing composites.

Spherical Gravity Forwarding of Global Discrete Grid Cells by Isoparametric Transformation

For regional or even global geophysical problems, the curvature of the geophysical model cannot be approximated as a plane, and its curvature must be considered. Tesseroids can fit the curvature, but their shapes vary from almost rectangular at the equator to almost triangular at the poles, i.e., degradation phenomena. Unlike other spherical discrete grids (e.g., square, triangular, and rhombic grids) that can fit the curvature, the Discrete Global Grid System (DGGS) grid can not only fit the curvature but also effectively avoid degradation phenomena at the poles. In addition, since it has only edge-adjacent grids, DGGS grids have consistent adjacency and excellent angular resolution. Hence, DGGS grids are the best choice for discretizing the sphere into cells with an approximate shape and continuous scale. Compared with the tesseroid, which has no analytical solution but has a well-defined integral limit, the DGGS cell (prisms obtained from DGGS grids) has neither an analytical solution nor a fixed integral limit. Therefore, based on the isoparametric transformation, the non-regular DGGS cell in the system coordinate system is transformed into the regular hexagonal prism in the local coordinate system, and the DGGS-based forwarding algorithm of the gravitational field is realized in the spherical coordinate system. Different coordinate systems have differences in the integral kernels of gravity fields. In the current literature, the forward modeling research of polyhedrons (the DGGS cell, which is a polyhedral cell) is mostly concentrated in the Cartesian coordinate system. Therefore, the reliability of the DGGS-based forwarding algorithm is verified using the tetrahedron-based forwarding algorithm and the tesseroid-based forwarding algorithm with tiny tesseroids. From the numerical results, it can be concluded that if the distance from observations to sources is too small, the corresponding gravity field forwarding results may also have ambiguous values. Therefore, the minimum distance is not recommended for practical applications.

Maxwell–Boltzmann-like neutron spectrum production for Maxwellian averaged cross sections measurements

Over the years, Maxwellian Averaged Cross Sections (MACS) have been measured by neutron activation, providing a neutron energy spectrum resembling the one found inside the stars. Recently, a new method has been proposed to produce stellar spectra at different stellar temperatures (a Maxwell–Boltzmann neutron energy distribution), employing the 7Li(p,n)7Be reaction. The method is based on the idea of shaping the proton beam energy to shape the neutron beam spectrum. This method was applied to obtain a well-reproduced Maxwell–Boltzmann neutron spectrum (MBNS) at kT=28 keV. An initial proton energy of 3170 keV and an aluminum foil as a proton energy shaper were employed. Differential angular neutron energy distributions from 0 to 90 degrees in 10∘ steps were measured to obtain the 0∘–90∘ integrated neutron spectrum over a neutron flight path of 50 cm. This manuscript reports on the measurement results, confirming the method’s capability, and suggests the approach for producing a high-quality MBNS at kT=28 keV.

The Spatial Characteristics of Intervertebral Foramina within the L4/L5 and L5/S1 Motor Segments of the Spine

The prevalence of lower back pain and radicular pain in the population requires more and more accurate diagnostic methods to more effectively prevent and treat patients with these ailments. In this paper, we focused on one of the causes of lower back pain and radicular pain—lumbar foraminal stenosis (LFS). The aim of the study is to assess the morphometry of the intervertebral canals in the lumbar spine at the levels of the L4/L5 and L5/S1 motor segments. The obtained results showed correlations between the circumference and the surface area on individual cross-sections of the intervertebral canals at the L4/L5 and L5/S1 levels and determined the approximate shape of the root canal and its variability. On this basis, we were able to determine the influences of the patient’s age and sex on the morphometric parameters of the intervertebral canals at the L4/L5 and L5/S1 levels. Further research is needed in this area, taking into account additional factors influencing the shape of intervertebral canals.

Multi-Tap Resistive Sensing and FEM Modeling Enables Shape and Force Estimation in Soft Robots

Multi-Tap Resistive Sensing and FEM Modeling Enables Shape and Force Estimation in Soft Robots