Shape Matching Research Articles

Real-time soft body dissection simulation with parallelized graph-based shape matching on GPU

Background and ObjectiveInteractive soft tissue dissection has been a fundamental procedure in virtual surgery systems. Existing cutting algorithms involve complex topology changes of simulation meshes, which can increase simulation overhead and produce visual artifacts. In this paper, we proposed a novel graph-based shape-matching method that allows for real-time, flexible, progressive, and discontinuous cuts on soft tissue. MethodsWe employed shape-matching constraints within the position-based dynamics (PBD) framework, a widely adopted approach for real-time simulation applications. The soft tissue was effectively modeled using overlapping clusters, each governed by shape-matching constraints. The dissection process was bifurcated into two distinct stages. In the first stage, the surgical scalpel presses the surface of the soft tissue. The soft tissue is cut apart when the surface pressure exceeds a threshold, entering the second stage. To address the discrepancy between the visual mesh and the simulation model during cluster separation, we developed an Aggregate Finding Connected Components (AFCC) algorithm, optimized for GPU computation and integrated with a background grid. This approach also avoids ghost forces and fragmentation artifacts. To control the increase in the number of clusters, we also propose a merging strategy that can run in parallel. ResultsOur simulation outcomes demonstrated that the AFCC dissection algorithm effectively manages cluster separation and expansion with robustness. There were no ghost forces between the cutting surface and unrealistic fragments. Our simulation capability extended to supporting intricate and discontinuous cutting routes. Our dissection simulation maintained real-time performance even with over 100,000 particles constituting the soft tissue. ConclusionsOur real-time and robust surgical dissection simulation technique enables the performance of complex cuts in various surgical scenarios, demonstrating its potential in virtual surgery applications.

The influence of sentence focus on mental simulation: A possible cause of ACE instability.

Recent studies have revealed the instability of the action-sentence compatibility effect (ACE). The current study was designed to demonstrate the hypothesis that the instability of the ACE may be attributed to the instability of focused information in a sentence. A pilot study indicated that the focused information of sentences was relatively stable in the sentence-picture verification task but exhibited significant interindividual variability in the action-sentence compatibility paradigm in previous studies. Experiments 1 and 2 examined the effect of sentence focus on the shape match effect and the ACE by manipulating the focused information of sentences using the focus marker word "" (is). Experiment 1 found that the shape match effect occurred in the original sentence, while it disappeared when the word "" (is) was used to make an object noun no longer the focus of a sentence. Experiment 2 failed to observe the ACE regardless of whether the sentence focus was on the action information. Experiment 3 modified the focus manipulation to observe its impact on the ACE using different fonts and underlines to highlight the focused information. The results indicated that the ACE only occurred when the action information was the sentence focus. These findings suggest that sentence focus influences mental simulation, and the instability of the ACE is likely to be associated with the instability of sentence focus in previous studies. This outcome highlights the crucial role of identifying specific information as the critical element expressed in the current linguistic context for successful simulation.

Topological isomer effect on one-step C2H4 purification from ternary C2 hydrocarbon mixture

One-step separation of C2H4 from ternary C2H2/C2H4/C2H6 hydrocarbon mixtures is desirable yet challenging in the industry due to their similar sizes and physical properties. Herein, we investigate the topological isomer effect on the relationship of pore environments and one-step separation of C2H4. IAM-8 and NU-912 are topological isomers that are obtained with the identical molecular formula [Zr6(μ3-O)8(H2O)8(PPTBA)2)]·(solvents)x (H4PPTBA = 4,4′,4′',4′''-(pyrrolo[3,2-b]pyrrole-1,2,4,5-tetrayl) tetrabenzoic acid) but with different topologies, (4,8)-connected sqc and scu, respectively. Both IAM-8 and NU-912 exhibit inverse adsorption behaviors. The IAM-8 shows excellent C2H6/C2H4 (v/v = 10/90) selectivity of 2.1 and a large uptake ratio of 120% (78.70/65.78 cm3 g−1) at 298 K and 1 bar. The NU-912 exhibits relatively lower C2H6/C2H4 (v/v = 10/90) selectivity of 1.5 and adsorption capacity (63.00/47.00 cm3 g−1) at 298 K and 1 bar. GCMC and DFT simulations show that the higher adsorption capacity of IAM-8 relative to that of NU-912, as well as the C2H6/C2H4 selectivity of IAM-8 over NU-912, is due to pore size and shape matching. Dynamic breakthrough experiments demonstrate that polymer-grade C2H4 (>99.9%) can be harvested in one step from a ternary mixture of C2H2/C2H6/C2H4 (1/9/90, v/v/v) for both materials, but indeed showing a better performance for IAM-8. This work is the first case to demonstrate the topological isomer effect to fulfill the pore matching requirement in promoting the challenging one-step C2H4 separation.

Locality Preserving Refinement for Shape Matching with Functional Maps

In this paper, we address the nonrigid shape matching with outliers by a novel and effective pointwise map refinement method, termed Locality Preserving Refinement. For accurate pointwise conversion from a given functional map, our method formulates a two-step procedure. Firstly, starting with noisy point-to-point correspondences, we identify inliers by leveraging the neighborhood support, which yields a closed-form solution with linear time complexity. After obtained the reliable correspondences of inliers, we refine the pointwise correspondences for outliers using local linear embedding, which operates in an adaptive spectral similarity space to further eliminate the ambiguities that are difficult to handle in the functional space. By refining pointwise correspondences with local consistency thus embedding geometric constraints into functional spaces, our method achieves considerable improvement in accuracy with linearithmic time and space cost. Extensive experiments on public benchmarks demonstrate the superiority of our method over the state-of-the-art methods. Our code is publicly available at https://github.com/XiaYifan1999/LOPR.

Compact HD Map Construction via Douglas-Peucker Point Transformer

High-definition (HD) map construction requires a comprehensive understanding of traffic environments, encompassing centimeter-level localization and rich semantic information. Previous works face challenges in redundant point representation or high-complexity curve modeling. In this paper, we present a flexible yet effective map element detector that synthesizes hierarchical information with a compact Douglas-Peucker (DP) point representation in a transformer architecture for robust and reliable predictions. Specifically, our proposed representation approximates class-agnostic map elements with DP points, which are sparsely located in crucial positions of structures and can get rid of redundancy and complexity. Besides, we design a position constraint with uncertainty to avoid potential ambiguities. Moreover, pairwise-point shape matching constraints are proposed to balance local structural information of different scales. Experiments on the public nuScenes dataset demonstrate that our method overwhelms current SOTAs. Extensive ablation studies validate each component of our methods. Codes will be released at https://github.com/sweety121/DPFormer.

Design and development of a personalized virtual reality-based training system for vascular intervention surgery

Background and objectivesVirtual training has emerged as an exceptionally effective approach for training healthcare practitioners in the field of vascular intervention surgery. By providing a simulated environment and blood vessel model that enables repeated practice, virtual training facilitates the acquisition of surgical skills in a safe and efficient manner for trainees. However, the current state of research in this area is characterized by limitations in the fidelity of blood vessel and guidewire models, which restricts the effectiveness of training. Additionally, existing approaches lack the necessary real-time responsiveness and precision, while the blood vessel models suffer from incompleteness and a lack of scientific rigor. MethodsTo address these challenges, this paper integrates position-based dynamics (PBD) and its extensions, shape matching, and Cosserat elastic rods. By combining these approaches within a unified particle framework, accurate and realistic deformation simulation of personalized blood vessel and guidewire models is achieved, thereby enhancing the training experience. Furthermore, a multi-level progressive continuous collision detection method, leveraging spatial hashing, is proposed to improve the accuracy and efficiency of collision detection. ResultsOur proposed blood vessel model demonstrated acceptable performance with the reduced deformation simulation response times of 7 ms, improving the real-time capability at least of 43.75 %. Experimental validation confirmed that the guidewire model proposed in this paper can dynamically adjust the density of its elastic rods to alter the degree of bending and torsion. It also exhibited a deformation process comparable to that of real guidewires, with an average response time of 6 ms. In the interaction of blood vessel and guidewire models, the simulator blood vessel model used for coronary vascular intervention training exhibited an average response time of 15.42 ms, with a frame rate of approximately 64 FPS. ConclusionsThe method presented in this paper achieves deformation simulation of both vascular and guidewire models, demonstrating sufficient real-time performance and accuracy. The interaction efficiency between vascular and guidewire models is enhanced through the unified simulation framework and collision detection. Furthermore, it can be integrated with virtual training scenarios within the system, making it suitable for developing more advanced vascular interventional surgery training systems.

Systematic prediction method for flip-chip bonding connectivity of ultra-large array infrared detector.

The flip-chip bonding technique utilized in ultra-large array infrared detectors has a substantial impact on connectivity rates. The electrical connectivity of the flip-chip bonding process exhibits randomness due to the difficulties in the surface control of large-scale devices. This restriction hinders the development of ultra-large array devices. In this work, the surface shape matching calculation is performed based on the surface shape distributions measured from infrared detector chips and readout circuits. The multi combinations and multi rotation angles are employed to calculate the distribution of combined surface distances, and the combined PV (peak-to-valley) value is applied to describe the severity of surface mismatch. Test devices with combined PV values ranging from 7.460 µm to 4.265 µm are prepared and tested, and the connectivity rate achieves an improvement from 74.57% to 99.75% between mismatched devices and matching devices. The electrical test results of test devices indicate that disconnections tend to cluster in areas where surface distance is over 5 µm, which is determined by extracting and analyzing the surface distance correlated to electrical test results. A standard based on the combined PV value is established to select matching combinations and ensure a high connectivity rate of 99% or 97% for infrared detectors, while the connectivity rates of randomly selected devices are no higher than 91%. This work presents a systematic method to predict and improve the connectivity rate of flip-chip bonding process for ultra-large array infrared detector.

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

Accurate identification of fruits in greenhouse environments is an essential need for the precise functioning of agricultural robots. This study presents a solution to the problem of distinguishing cucumber fruits from their stems and leaves, which often have similar colors in their natural environment. The proposed algorithm for cucumber fruit identification relies on color segmentation and form matching. First, we get the boundary details from the acquired image of the cucumber sample. The edge information is described and reconstructed by utilizing a shape descriptor known as the Fourier descriptor in order to acquire a matching template image. Subsequently, we generate a multi-scale template by amalgamating computational and real-world data. The target image is subjected to color conditioning in order to enhance the segmenacktation of the target region inside the HSV color space. Then, the segmented target region is compared to the multi-scale template based on its shape. The method of color segmentation decreases the presence of unwanted information in the target image, hence improving the effectiveness of shape matching. An analysis was performed on a set of 200 cucumber photos that were obtained from the field. The findings indicate that the method presented in this study surpasses conventional recognition algorithms in terms of accuracy and efficiency, with a recognition rate of up to 86%. Moreover, the system has exceptional proficiency in identifying cucumber targets within greenhouses. This attribute renders it a great resource for offering technical assistance to agricultural robots that operate with accuracy.

Improved biharmonic kernel signature for 3D non-rigid shape matching and retrieval

Improved biharmonic kernel signature for 3D non-rigid shape matching and retrieval

Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey.

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.

Novel Anticounterfeiting Solution Based on 2D Materials Produced by Electrochemical Exfoliation.

This work demonstrates the use of 2D materials (2DMs) as identification tags by exploiting their unique shape. Electrochemical exfoliation enables the production of large quantities of optically accessible 2DMs with diverse morphology and large lateral sizes up to 20µm. Image processing techniques are used to facilitate shape identification and matching within a dataset of 500 unique nanosheets. Rotational and translation invariant shape matching with no false positive matches between over 100 000 unique shape pairings is shown. The approach enables individual nanosheets to be deposited onto products, such as packaging of luxury goods, pharmaceuticals, banknotes, etc., as a unique seal of authenticity. Quick inspection of the nanoscale tag by optical microscopy allows the shape to be compared against the genuine dataset, enabling unique identification. The optical features of 2D materials, such as Raman and/or photoluminescence signals can be used as an additional chemical fingerprint, making the anticounterfeiting solution very robust.

Low-Entropy Hydration Shells at the Spike RBD's Binding Site May Reveal the Contagiousness of SARS-CoV-2 Variants.

The infectivity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is primarily determined by the binding affinity between the receptor-binding domain (RBD) of the spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor. Here, through screening off pseudo hydrophilic groups on protein surfaces, the distribution of low-entropy regions on hydration shells of the ACE2 receptor and the RBDs of multiple SARS-CoV-2 variants was demonstrated. Shape matching between the low-entropy hydration shells of multiple SARS-CoV-2 variants and the ACE2 receptor has been identified as a mechanism that drives hydrophobic attraction between the RBDs and the ACE2 receptor, which estimates the binding affinity. Low-entropy regions of the hydration shells, which play important roles in determining the binding of other viruses and their receptors, are demonstrated. The RBD-ACE2 binding is thus found to be guided by hydrophobic collapse between the shape-matched low-entropy regions of the hydration shells of the proteins. A measure of the low-entropy status of the hydration shells can be estimated by calculating genuine hydrophilic groups within the binding sites. An important indicator of the contagiousness of SARS-CoV-2 variants is the low-entropy level of its hydration shells at the spike protein binding site.

An algorithm for extracting similar segments of moving target trajectories based on shape matching

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results.

An operator-splitting approach for variational optimal control formulations for diffeomorphic shape matching

We present formulations and numerical algorithms for solving diffeomorphic shape matching problems. We formulate shape matching as a variational problem governed by a dynamical system that models the flow of diffeomorphism ft∈diff(R3). We overview our contributions in this area, and present an improved, matrix-free implementation of an operator splitting strategy for diffeomorphic shape matching. We showcase results for diffeomorphic shape matching of real clinical cardiac data in R3 to assess the performance of our methodology.

A RANDOMIZED CONTROLLED TRIAL OF KINEMATICALLY AND MECHANICALLY ALIGNED TOTAL KNEE ARTHROPLASTY: 12-YEAR FOLLOW-UP

AbstractIntroductionThe optimal alignment technique for total knee replacement (TKR) remains controversial. We previously reported six-month and two-year results of a randomized controlled trial comparing kinematically (KA) versus mechanically (MA) aligned TKR. In the present study, we report 12-year results from this trial.MethodsThe original cohort included 88 TKRs (44 KA using Shape Match patient-specific guides and 44 MA using conventional instrumentation), performed from 2008 to 2009. After IRB approval, the health record of the original 88 patients were queried. Revisions, re-operations, and complications were recorded. The non-deceased patients were contacted via phone. Reoperation and complications were documented via the patient's history. Further, a battery of patient-reported outcome measures (including patient satisfaction, WOMAC, Oxford, KOOS Jr, Forgotten Joint Score, and M-SANE) were obtained.ResultsOf the original 88 patients in the study, 15 patients had a least one reoperation (17%). Patella problems were the most common cause of reoperation accounting for 5/8 reoperations in the KA group versus 3/7 in the MA group. There was no statistically significant difference between the two alignment methods in terms of major and minor complications or reoperations. At the 12-year follow-up, 26 patients died leaving 62 patients for follow-up. Of these, 48 patients (77%) were successfully contacted. The kinematically aligned total knees self-reported better satisfaction (96% versus 82%), but no difference in other patient-reported outcome measures compared to mechanically aligned TKRs.ConclusionKA TKR demonstrates excellent mid to long-term results compared to MA TKR with similar reoperations, complications, and patient-reported outcome measures.

Partial Matching of Nonrigid Shapes by Learning Piecewise Smooth Functions

AbstractLearning functions defined on non‐flat domains, such as outer surfaces of non‐rigid shapes, is a central task in computer vision and geometry processing. Recent studies have explored the use of neural fields to represent functions like light reflections in volumetric domains and textures on curved surfaces by operating in the embedding space. Here, we choose a different line of thought and introduce a novel formulation of partial shape matching by learning a piecewise smooth function on a surface. Our method begins with pairing sparse landmarks defined on a full shape and its part, using feature similarity. Next, a neural representation is optimized to fit these landmarks, efficiently interpolating between the matched features that act as anchors. This process results in a function that accurately captures the partiality. Unlike previous methods, the proposed neural model of functions is intrinsically defined on the given curved surface, rather than the classical embedding Euclidean space. This representation is shown to be particularly well‐suited for representing piecewise smooth functions. We further extend the proposed framework to the more challenging part‐to‐part setting, where both shapes exhibit missing parts. Comprehensive experiments highlight that the proposed method effectively addresses partiality in shape matching and significantly outperforms leading state‐of‐the‐art methods in challenging benchmarks. Code is available at https://github.com/davidgip74/Learning-Partiality-with-Implicit-Intrinsic-Functions

Attention And Positional Encoding Are (Almost) All You Need For Shape Matching

AbstractThe fast development of novel approaches derived from the Transformers architecture has led to outstanding performance in different scenarios, from Natural Language Processing to Computer Vision. Recently, they achieved impressive results even in the challenging task of non‐rigid shape matching. However, little is known about the capability of the Transformer‐encoder architecture for the shape matching task, and its performances still remained largely unexplored. In this paper, we step back and investigate the contribution made by the Transformer‐encoder architecture compared to its more recent alternatives, focusing on why and how it works on this specific task. Thanks to the versatility of our implementation, we can harness the bi‐directional structure of the correspondence problem, making it more interpretable. Furthermore, we prove that positional encodings are essential for processing unordered point clouds. Through a comprehensive set of experiments, we find that attention and positional encoding are (almost) all you need for shape matching. The simple Transformer‐encoder architecture, coupled with relative position encoding in the attention mechanism, is able to obtain strong improvements, reaching the current state‐of‐the‐art.

Unsupervised Learning of Robust Spectral Shape Matching

We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-wise maps often yields sub-optimal performance. In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing. Our approach obtains accurate correspondences not only for near-isometric shapes, but also for more challenging non-isometric shapes and partial shapes, as well as shapes with different discretisation or topological noise. Using a total of nine diverse datasets, we extensively evaluate the performance and demonstrate that our method substantially outperforms previous state-of-the-art methods, even compared to recent supervised methods. Our code is available at https://github.com/dongliangcao/Unsupervised-Learning-of-Robust-Spectral-Shape-Matching.

Pulsating heat pipe and embedded heat pipe heat spreaders for modular electronics cooling

Two-phase-based heat spreaders are highly sought electronics cooling solutions due to their superior thermal performance over conventional conduction plates. Along with good thermal performance, Pulsating Heat Pipes (PHPs) potentially have thickness, shape matching and cost benefits over Embedded Heat Pipe (EHP) heat spreader solutions. In this manuscript, the thermal performance of PHP charged with three different fluids-propylene, R245fa, and acetone was experimentally determined for different sink temperatures and compared to an EHP heat spreader. A mathematical model to predict the thermal performance of the PHP was developed and validated against experimental results. It was determined that at low sink temperatures, PHP charged with propylene performs better than or similar to an EHP heat spreader with up to 7.8 times higher effective thermal conductivity over a conduction plate. However, at moderate temperatures above >40 °C, propylene PHP dries out faster than the EHP plate. PHP performance with R245fa and acetone improves with increasing heater power. Effective thermal conductivity improved by 2.5 times with R245fa PHP and by 4 times with acetone PHP over the conventional plate but was lower than the EHP plate whose effective thermal conductivity was 17.3 times higher. However, weight comparison showed PHP to be lighter than the EHPs.

Flexible 3D Lane Detection by Hierarchical Shape Matching

As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. Specifically, we design a hierarchical network predicting flexible representations of lane shapes at different levels, simultaneously collecting global instance semantics and avoiding local errors. In the global scope, we propose to regress parametric curves w.r.t adaptive axes that help to make more robust predictions towards complex scenes, while in the local vision the structure of lane segment is detected in each of the dynamic anchor cells sampled along the global predicted curves. Moreover, corresponding global and local shape matching losses and anchor cell generation strategies are designed. Experiments on two datasets show that we overwhelm current top methods under high precision standards, and full ablation studies also verify each part of our method. Our codes will be released at https://github.com/Doo-do/FHLD.