Shape Reconstruction Algorithm Research Articles

Structural deformation and clamping force monitoring of reconfigurable tooling motivated by strain data in aircraft assembly

The quality of aircraft assembly is mainly guaranteed by toolings which are vital to the geometrical accuracy and service performance of aviation products. In this research, a real-time monitoring system that determines the structural deformation and clamping force of reconfigurable toolings using strain data is developed to perceive the service state of the toolings. By laying fiber Bragg gratings on positioning beam and baseplate of the reconfigurable tooling, strain data of the tooling structures are gauged and transferred to curvatures. The beam and baseplate are modeled as one and two-dimensional objects respectively and shape reconstruction algorithms are established to obtain their deflection curve and surface using curvature information. Distribution of fibers is optimized to minimize the conversion error from strain to curvature. An estimation that reveals the mathematical relationship between the shape reconstruction error and measurement interval is implemented, and a mapping model from strains to clamping force of the beam is established. These algorithms are integrated into the self-developed monitoring software and undergo simulating and experimental tests. The maximum relative errors of deformation and force are 4.53% and 4.12% respectively in simulation, and 9.21% and 7.29% individually in experiment, which validates the efficiencies of the method. Tests of the monitoring system suggest that it can provide a timely and accurate sensing of the deformation and force of the tooling.

ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation

Accurate human shape recovery from a monocular RGB image is a challenging task because humans come in different shapes and sizes and wear different clothes. In this paper, we propose ShapeBoost, a new human shape recovery framework that achieves pixel-level alignment even for rare body shapes and high accuracy for people wearing different types of clothes. Unlike previous approaches that rely on the use of PCA-based shape coefficients, we adopt a new human shape parameterization that decomposes the human shape into bone lengths and the mean width of each part slice. This part-based parameterization technique achieves a balance between flexibility and validity using a semi-analytical shape reconstruction algorithm. Based on this new parameterization, a clothing-preserving data augmentation module is proposed to generate realistic images with diverse body shapes and accurate annotations. Experimental results show that our method outperforms other state-of-the-art methods in diverse body shape situations as well as in varied clothing situations.

Shape detection of thin diameter flexible sensors with non-uniform gratings

Flexible intelligent medical device shape feedback has a very important application prospect in intelligent structure detection. Due to the error accumulation effect, the distribution of gratings is also an important factor affecting the reconstruction error of shape sensors. A grating positions optimization method based on the fusion of adaptive mutation particle swarm optimization algorithm and shape reconstruction algorithm is proposed in the paper. The curves with 10 measurement points arranged at a sensing length of 810 mm and 4 measurement points arranged at a sensing length of 200 mm are simulated and calculated. After multiple optimization training, the sparse optimal distribution of gratings with the smallest shape error is obtained. Package sensors with uniform and non-uniform distribution of grating points based on the obtained point positions, and conduct planar shape reconstruction experiments. After multiple shape reconstruction experiments, the shape errors of the sensor shape reconstruction endpoint for uniform gratings are 2.76% and 2.94%, respectively, and the reconstruction accuracy for non-uniform gratings is 1.94% and 2.71%. The optimized shape sensor has good performance, thus proving the effectiveness of the fiber grating sensing grid point optimization method in flexible medical device deformation monitoring.

Spherical wave illumination scanning digital holographic profilometry.

In this work, we proposed what we believe to be a novel scanning solution for the assessment of high-NA samples, referred to as spherical-wave illumination scanning digital holographic profilometry (SWS-DHP). This approach introduces a 2F optimization methodology, based on the measurement of the focal length of the object to determine the spherical component of the scanning. Furthermore, re-optimization of 2F, whether it needs to be operated depends on the measured object's NA to inspect more information. Meanwhile, utilizing phase space analysis shows SWS superiority in information transfer for high-NA samples compared to plane-wave illumination scanning. In addition, this method introduces a shape reconstruction algorithm with volumetric aberration compensation based on the propagation of the aberrated object and illumination waves to obtain high-quality measurements. Finally, the imaging merits of SWS-DHP were proved through simulations and were experimentally verified for the object of NA up to 0.87.

A novel algorithm for three-dimensional shape reconstruction for microscopic objects based on shape from focus

A novel algorithm for three-dimensional shape reconstruction for microscopic objects based on shape from focus

Dot to dot, simple or sophisticated: a survey on shape reconstruction algorithms

Dot to dot, simple or sophisticated: a survey on shape reconstruction algorithms

Scan-free time-of-flight-based three-dimensional imaging through a scattering layer.

Reconstructing an object's three-dimensional shape behind a scattering layer with a single exposure is of great significance in real-life applications. However, due to the little information captured by a single exposure while strongly perturbed by the scattering layer and encoded by free-space propagation, existing methods cannot achieve scan-free three-dimensional reconstruction through the scattering layer in macroscopic scenarios using a short acquisition time of seconds. In this paper, we proposed a scan-free time-of-flight-based three-dimensional reconstruction method based on explicitly modeling and inverting the time-of-flight-based scattering light propagation in a non-confocal imaging system. The non-confocal time-of-flight-based scattering imaging model is developed to map the three-dimensional object shape information to the time-resolved measurements, by encoding the three-dimensional object shape into the free-space propagation result and then convolving with the scattering blur kernel derived from the diffusion equation. To solve the inverse problem, a three-dimensional shape reconstruction algorithm consisting of the deconvolution and diffractive wave propagation is developed to invert the effects caused by the scattering diffusion and the free-space propagation, which reshapes the temporal and spatial distribution of scattered signal photons and recovers the object shape information. Experiments on a real scattering imaging system are conducted to demonstrate the effectiveness of the proposed method. The single exposure used in the experiment only takes 3.5s, which is more than 200 times faster than confocal scanning methods. Experimental results show that the proposed method outperforms existing methods in terms of three-dimensional reconstruction accuracy and imaging limit subjectively and objectively. Even though the signal photons captured by a single exposure are too highly scattered and attenuated to present any valid information in time gating, the proposed method can reconstruct three-dimensional objects located behind the scattering layer of 9.6 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of 19.2 TMFPs.

Simultaneous Shape and Tip Force Sensing for the COAST Guidewire Robot.

Placement of catheters in minimally invasive cardiovascular procedures is preceded by navigating to the target lesion with a guidewire. Traversing through tortuous vascular pathways can be challenging without precise tip control, potentially resulting in the damage or perforation of blood vessels. To improve guidewire navigation, this paper presents 3D shape reconstruction and tip force sensing for the COaxially Aligned STeerable (COAST) guidewire robot using a triplet of adhered single core fiber Bragg grating sensors routed centrally through the robot's slender structure. Additionally, several shape reconstruction algorithms are compared, and shape measurements are utilized to enable tip force sensing. Demonstration of the capabilities of the robot is shown in free air where the shape of the robot is reconstructed with average errors less than at the guidewire tip, and the magnitudes of forces applied to the tip are estimated with an RMSE of or less.

Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks.

Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical methods for the EIT inverse problem. Despite the success, numerical instability is still a major hurdle due to many factors, including the discretization error of the problem. Furthermore, most algorithms with good performance are relatively time consuming and do not allow real-time applications. In our approach, the goal is to separate the unknown conductivity into two regions, namely the region of homogeneous background conductivity and the region of non-homogeneous conductivity. Therefore, we pose and solve the problem of shape reconstruction using machine learning. We propose a novel and simple jet intriguing neural network architecture capable of solving the EIT inverse problem. It addresses previous difficulties, including instability, and is easily adaptable to other ill-posed coefficient inverse problems. That is, the proposed model estimates the probability for a point of whether the conductivity belongs to the background region or to the non-homogeneous region on the continuous space Rd∩Ω with d∈{2,3}. The proposed model does not make assumptions about the forward model and allows for solving the inverse problem in real time. The proposed machine learning approach for shape reconstruction is also used to improve gradient-based methods for estimating the unknown conductivity. In this paper, we propose a piece-wise constant reconstruction method that is novel in the inverse problem setting but inspired by recent approaches from the 3D vision community. We also extend this method into a novel constrained reconstruction method. We present extensive numerical experiments to show the performance of the architecture and compare the proposed method with previous analytic algorithms, mainly the monotonicity-based shape reconstruction algorithm and iteratively regularized Gauss-Newton method.

Accuracy of a Smartphone-based freeware solution and two shape reconstruction algorithms in log volume measurements

Enhancing accuracy, mobility, safety, ergonomics, data transfer and traceability functions in log measurement applications are prerequisites of modern business in the wood supply chain. Various methods and methodologies have been tested so far to overcome the current limitations in the attempt to move the wood supply chain to Forestry 4.0 concepts, many of which still share the same problem, namely the high costs incurred by data acquisition. A freeware application was used in this study to collect point cloud data on 612 logs of various sizes and two external shape reconstruction algorithms, namely Random Sampling and Consensus (RANSAC) and Poisson interpolation, were then used to produce volume estimates under a mobile scanning approach (MS). These were compared against the estimates produced by a highly-detailed manual measurement (MM). At the sample level, which included logs reaching volumes of more than 3.5 m3, the differential bias of the methods ranged between 0.037 and 0.075 m3, while the errors metrics such as the mean absolute error (MAE), mean squared error (MSE) and root mean square error (RMSE) were between 0.047 and 0.081, 0.006 to 0.023 and 0.076 to 0.151 m3. The results indicate that, although there was a proportional bias in MS estimates, the approach would be useful for logs in size of 0.25 to 0.40 m3, while these log sizes largely resemble the characteristics of the wood supply in many areas of the world. Further studies should focus on the capability and accuracy of the tested application to instantly produce accurate estimates, as well as on the integration of deep learning to produce better estimates.

Parameter Extraction and Performance Analysis of 3D Surface Reconstruction Techniques

Digital image-based 3D surface reconstruction is a streamlined and proper means of studying the features of the object being modelled. The generation of true 3D content is a very crucial step in any 3D system. A methodology to reconstruct a 3D surface of objects from a set of digital images is presented in this paper. It is simple, robust, and can be freely used for the construction of 3D surfaces from images. Digital images are taken as input to generate sparse and dense point clouds in 3D space from the detected and matched features. Poisson Surface, Ball Pivoting, and Alpha shape reconstruction algorithms have been used to reconstruct photo-realistic surfaces. Various parameters of these algorithms that are critical to the quality of reconstruction are identified and the effect of these parameters with varying values is studied. The results presented in this study will give readers an insight into the behaviour of various algorithmic parameters with computation time and fineness of reconstruction.

Three-dimensional numerical simulation of rime ice accumulation on silicone rubber insulator and its experimental verification in the natural environment

Icing on transmission lines is a challenging problem in the world. Rime icing on silicone rubber insulator was taken as research object in this paper. The position coordinates of supercooled water droplets collided with the surface of the insulator were extracted, the local collision coefficient (E) of the droplet was calculated by a triangular area ratio method. The “point-line-face-solid” ice shape reconstruction algorithm was proposed to realize three-dimensional numerical simulation of rime ice accretion on the insulator. The results shown that the effect of median volume diameter (MVD) of the droplet on the E is greater than that of wind speed (v). The most serious icing occurs on the rod and the edge of the shed. Due to the shielding effect of the shed, the icing on the rod presents the shape of the nose. The length, thickness and amount of rime icing on the insulator calculated by the model increases with time, whose average errors with that obtained by the tests are 7.68%, 7.96%, 10.34%, and 9.45%, respectively. Moreover, the simulated icing distribution is well consistent with the experimental results. Therefore, the effectiveness of the numerical simulation method was proved in the present research.

Optical fiber shape sensing of flexible medical instruments with temperature compensation

Needle shape monitoring techniques ensure the safety of modern interventional surgery. Optical fiber sensors such as fiber Bragg grating (FBG) are being increasingly employed for needle shape sensing. However, existing studies have overlooked the simultaneous strain and temperature sensitivities of the sensors in the fiber grating sensing array, directly affecting the shape reconstruction accuracy under variable temperature environments. Thus, this study proposed a method to optimize shape reconstruction-based fiber optic sensing by introducing a strain and temperature sensitivity matrix. First, the relationship between the wavelength shift of the fiber grating implanted in the needle and its strain and temperature was analyzed based on the fiber grating sensing theory. Thereafter, the strain sensitivity matrix was introduced to improve the accuracy of the shape reconstruction algorithm. Moreover, temperature compensation eliminated temperature influence on the sensor. Next, the local unit geometric parameter relationship and coordinate transformation equation of the needle were derived, and the shape sensing model of the FBG array-based needle was established. Finally, the effectiveness of the method was verified by conducting variable temperature shape reconstruction experiments. The proposed method had increased accuracy over the conventional method in shape reconstruction under variable temperature environments, thus indicating its superiority and application feasibility.

COD: an algorithm for shape reconstruction of transiting celestial bodies through topological optimization

ABSTRACT We introduce a novel algorithm, COD – Compact Opacity Distribution, for shape reconstruction of a celestial body that has been observed to occult a star, using the photometric time-series observations of the occultation. COD finds a solution to the light-curve inversion problem for an optically thick occulter having an approximately convex shape, together with an estimate of its size, impact parameter and velocity, relative to the occulted star. The algorithm is based on an optimization scheme that uses topological constraints and an objective function for the geometry of the occulter. The constraints of the problem follow linear relations, which enable the use of linear programming optimization as the mathematical framework. Multiple tests of the algorithm were performed, all of which resulted in high correlations between the simulated and obtained shapes of the occulting objects, with errors within $5{{\ \rm per\ cent}}$ in their projected velocities and horizontal sizes, and within 0.1 in their impact parameters. These tests include a video of a solar eclipse by Phobos, as seen by NASA’s Curiosity rover, which was collapsed into its corresponding light curve and reconstructed afterwards. We applied COD to the mysterious case of VVV-WIT-08 – a single deep occultation ($\sim 96 {{\ \rm per\ cent}}$) of a giant star lasting for over 200 d. The analysis, which did not assume any specific shape of the occulter, suggested an object with a projected opacity distribution resembling an ellipse with an eccentricity of ∼0.5, tilted at ∼30 degrees relative to the direction of motion, with a semi-minor axis similar to the stellar radius.

A Framework for Array Shape Reconstruction Through Mutual Coupling

Flexible phased arrays potentially enable diverse applications not permitted by rigid systems; however, they introduce ambiguity in antenna element positions. If this position ambiguity can be overcome, flexible arrays can perform the full suite of array functions: beam steering, wavefront engineering, and beam focusing. Furthermore, shape reconstructions of arrays can be used for applications beyond beamforming. We propose a framework to reconstruct the shape of a flexible array that only uses mutual coupling measurements and does not require additional sensors or functionalities in the system. We discuss the approach, a two-step algorithm, which is highly modular and can be implemented in a variety of phased array systems. To demonstrate the accuracy of the approach, we present results from two passive 2.5-GHz phased array setups using dipole and patch antennas, as well as a 10-GHz (active) integrated circuit flexible phased array, and demonstrate the accuracy of the approach in this system. In all cases, the algorithm reconstructs the antenna shape accurately, with average position errors of approximately 6% of the wavelength. This article can serve as the beginning of the broad study of shape reconstruction algorithms and their applications.

A Small and High-Speed Driving Mechanism for 3D Shape Measurement in Monocular Endoscopy.

Three-dimensional (3D) shape acquisition has been widely introduced to enrich quantitative analysis with the combination of object shape and texture, for example, surface roughness evaluation in industry and gastrointestinal endoscopy in medicine. Shape from focus is a promising technique to measure substance surfaces in 3D space because no occlusion problem appears in principle, as does with stereo shape measurement, which is another commonly used option. We have been developing endoscopic shape measurement devices and shape reconstruction algorithms. In this paper, we propose a mechanism for driving an image sensor reciprocated for the shape from focus of 3D shape measurement in monocular endoscopy. It uses a stepping motor and a planar-end cam, which transforms the motor rotation to imaging sensor reciprocation, to implement the shape from focus of 3D shape measurement in endoscopy. We test and discuss the device in terms of its driving accuracy and application feasibility for endoscopic 3D shape measurement.

Surveying a Floating Iceberg With the USV SEADRAGON

The calving, drifting, and melting of icebergs has local, regional, and global implications. Besides the impacts to local ecosystems due to changes in seawater salinity and temperature, the freshwater influx and transport can have significant regional effects related to the ocean circulation. The increased influx of freshwater ice due to increase calving from ice shelves and the destabilization of the continental ice sheet will affect sea levels globally. In addition, drifting icebergs pose threats to offshore operations because they could damage offshore installations, e.g., pipelines and subsea manifolds, and interrupt marine transportation. Iceberg drift and deterioration models have been developed to better predict climate change and protect offshore operations. Iceberg shape is one of the most critical parameters in these models, but it is challenging to obtain because of iceberg movement caused by winds, waves, and currents. In this paper, we present an algorithm for iceberg motion estimation and shape reconstruction based on in-situ point cloud measurements. The algorithm is developed based on point cloud matching strategies, policy-based optimization, and Kalman filtering. A down-sampling method is also integrated to reduce the processing time for possible real-time applications. The motion estimation algorithm is applied to a simulated data set and field measurements collected by an Unmanned Surface Vehicle (USV) on a free-floating, translating, and rotating, iceberg. In the field data, the above-water iceberg surface was measured with a scanning LIDAR, while the below-water portion (0–50 m) was profiled using a side-looking multi-beam sonar. When applying the motion estimation algorithm to these two independent point cloud measurements collected by the two sensing modalities, consistent iceberg motion estimates are obtained. The resulting motion estimates are then used to reconstruct the iceberg shape. During the field experiment, additional oceanographic measurements, such as temperature, ocean current, and wind, were collected simultaneously by the USV. We have observed water upwelling and a colder and fresher water plume at the sea surface downstream the iceberg. Combining the iceberg shape rendering and the surrounding environmental measurements, we estimated the iceberg melting parameters due to the sensible heat flux and surface wave erosion at different iceberg sections.

A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

Supershape Augmented Reconstruction Method Based on Boolean Operations in Electrical Impedance Tomography

This paper presents a supershape augmented shape reconstruction algorithm for Electrical impedance tomography, aiming at high-quality reconstructions with respect to multiphase conductivity distributions. Within the proposed algorithm, the reconstructed inclusions are composed of basic shape primitives expressed through a supershape formula. To allow automatic topological evolution, multiple Boolean operations, including three basic Boolean operations and compound Boolean operations, are employed so that the candidate shape primitives can be intersected, subtracted and merged to form different shapes. The proposed algorithm is tested using simulations and water tank experimental data. Moreover, robustness studies considering varying initial guesses and differing signal to noise ratios are performed. It is demonstrated that the proposed algorithm is able to preserve detailed features of inclusions under detection and provide reliable estimates to unknown conductivity values and the interface properties.

Shape reconstruction of delamination defects using thermographic infrared signals based on an enhanced Canny approach

he Canny shape-reconstruction algorithm is combined with the thermographic signal reconstruction (TSR) technique for the characterisation of delamination defects in planar multilayer structures. The adopted approach consists in fitting the raw thermographic images to a low-degree polynomial in the log–log representation of the time axis, according to TSR technique, and then apply the Canny algorithm to the reconstruction of the original signals. The proposed juxtaposition of the two treatments benefits from the inherent signal de-noising and compression of the TSR algorithm, thus enhancing the performance of the overall reconstruction process. The proposed approach has been applied using synthetic signals obtained via a 3D semi-analytical simulation code and the obtained reconstructed images have been compared with the (known) delamination geometry demonstrating satisfactory results.