Query: shape matching

title: A hybrid topological and shape matching approach for structure analysis.
abstract: Properties of crystalline and amorphous materials are characterized by the underlying long range and local crystalline order. Deformations and defects are structural hallmarks of plasticity, ice formation, and crystal growth mechanisms. Partitioning topological networks into constituent crystal building blocks, which is the basis of topological identification criteria, is an intuitive approach for classification in both bulk and confinement. However, techniques reliant on the convex hull for assigning orientations of component units fail for non convex blocks. Here, we propose a new framework, called Topological Unit Matching (TUM) which exploits information from topological criteria for an efficient shape matching procedure. TUM is a general family of algorithms, capable of quantifying deformations and unambiguously determining grains of bulk and confined ice polymorphs. We show that TUM significantly improves the identification of quasi one dimensional ice by including deformed prism blocks. We demonstrate the efficacy of TUM by analyzing supercooled water nanoparticles, amorphous ice, and phase transitions in an ice nanotube. We also illustrate the superiority of TUM in resolving topological defect structures with minimal parameterization.

title: Shape Matching GAN+ Scale Controllable Dynamic Artistic Text Style Transfer.
abstract: Dynamic artistic text style transfer aims to migrate the style in terms of both the appearance and motion patterns from a reference style video to the target text to create artistic text animation. Recent researches have improved the usability of transfer models by introducing texture control. However, it remains an important open challenge to investigate the control of the stylistic degree with respect to shape deformation. In this paper, we explore a new problem of dynamic artistic text style transfer with glyph stylistic degree control. The key idea is to build multi scale glyph style shape mappings through a novel bidirectional shape matching framework. Following this idea, we first introduce a scale ware Shape Matching GAN to learn such mappings to simultaneously model the style shape features at multiple scales and transfer them onto the target glyph. Furthermore, an advanced Shape Matching GAN+ is proposed to animate a static text image based on the reference style video. Our Shape Matching GAN+ characterizes the short term consistency of motion patterns via shape matchings within consecutive frames, which are propagated to achieve effective long term consistency. Experiments show that the proposed method outperforms previous state of the arts both qualitatively and quantitatively, and generate high quality and controllable artistic text.

title: Q Match: Iterative Shape Matching via Quantum Annealing
abstract: Finding shape correspondences can be formulated as anNP hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density. A promising research direction is to tackle such quadratic optimization problems over binary variables with quantum annealing, which, in theory, allows to find globally optimal solutions relying on a new computational paradigm. Unfortunately, enforcing the linear equality constraints in QAPs via a penalty significantly limits the success probability of such methods on currently available quantum hardware. To address this limitation, this paper proposes Q Match, i.e. a new iterative quantum method for QAPs inspired by the a expansion algorithm, which allows solving problems of an order of magnitude larger than current quantum methods. It works by implicitly enforcing the QAP constraints by updating the current estimates in a cyclic fashion. Further, Q Match can be applied for shape matching problems iteratively, on a subset of well chosen correspondences, allowing us to scale to real world problems. Using the latest quantum annealer, the D Wave Advantage, we evaluate the proposed method on a subset of QAPLIB as well as on isometric shape matching problems from the FAUST dataset.

title: Isometric Multi Shape Matching Supplementary Material
abstract: We present a novel approach for isometric multi shape matching based on matching each shape to a (virtual) universe shape (shown semi transparent) Our formulation represents point to point correspondences between shapes i and j as the composition of the shape to universe permutation matrix Pi and the universe to shape permutation matrix P j By doing so, the pairwise matchings Pij PiP j are by construction cycle consistent.  Our formulation successfully solves isometric multi matching of partial shapes.  Due to the cycle consistency we can use our correspondences to faithfully transfer textures across a shape collection.

title: Toward Realistic Virtual Try on Through Landmark Guided Shape Matching
abstract: Image based virtual try on aims to synthesize the customer image with an in shop clothes image to acquire seamless and natural try on results, which have attracted increasing attentions. The main procedures of image based virtual try on usually consist of clothes image generation and try on image synthesis, whereas prior arts cannot guarantee satisfying clothes results when facing large geometric changes and complex clothes patterns, which further deteriorates the afterwards tryon results. To address this issue, we propose a novel virtual try on network based on landmark guided shape matching (LM VTON) Specifically, the clothes image generation progressively learns the warped clothes and refined clothes in an end to end manner, where we introduce a landmarkbased constraint in Thin Plate Spline (TPS) warping to inject finer deformation constraints around the clothes. The try on process synthesizes the warped clothes with personal characteristics via a semantic indicator. Qualitative and quantitative experiments on two public datasets validate the superiority of the proposed method, especially for challenging cases such as large geometric changes and complex clothes patterns. Code will be available at https:/github.com/lgqfhwy/LM VTON.

title: The shape matching element method
abstract: We introduce a new method for direct physics based animation of volumetric curved models, represented using NURBS surfaces. Our technical contribution is the Shape Matching Element Method (SEM) SEM is a completely meshless algorithm, the first to simultaneously be robust to gaps and overlaps in geometry, be compatible with standard constitutive models and time integration schemes, support contact and frictional interactions and to preserve feature correspondence during simulation which enables editable simulated output. We demonstrate the efficacy of our algorithm by producing compelling physics based animations from a variety of curved input models.

title: Isometric Multi Shape Matching
abstract: Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi matchings that are by construction provably cycle consistent. We demonstrate the superior performance of our method on various datasets and set the new state of the art in isometric multi shape matching.

title: The shape matching element method
abstract: We introduce a new method for direct physics based animation of volumetric curved models, represented using NURBS surfaces. Our technical contribution is the Shape Matching Element Method (SEM) SEM is a completely meshless algorithm, the first to simultaneously be robust to gaps and overlaps in geometry, be compatible with standard constitutive models and time integration schemes, support contact and frictional interactions and to preserve feature correspondence during simulation which enables editable simulated output. We demonstrate the efficacy of our algorithm by producing compelling physics based animations from a variety of curved input models.

title: Evaluation of an Integer Optimized Shape Matching Algorithm
abstract: Computer vision and machine learning algorithms are often used for quality control for industrial products. Nowadays, neural networks can perform very well to detect the desired objects. Sometimes, the system has limited resources and is not capable of processing complex algorithms or use neural networks. Here, simpler algorithms are used for shape or object detection. The scope of the present work is to even lower the complexity of the shape matching algorithm by converting a shape detection algorithm to an integer version and evaluate the results. This allows to remove floating point units (FPU) of processors and reduce the area of a System on Chip (SoC) design of a smart image sensor.

title: Deformable MRI US image registration using biomechanically constrained shape matching network for image guided prostate intervention
abstract: MRI and Transrectal Ultrasound (TRUS) prostate images provide essential information for prostate intervention and cancer treatment. It is beneficial to register the MRI with TRUS images for cancer diagnosis and tumor delineation. However, the two imaging modalities have distinct image intensity which makes the registration difficult. A deep learning based image registration framework was proposed to perform MRI TRUS image fusion. The prostate from the MRI and TRUS was first segmented for prostate shape modeling using point clouds. The network utilizes point cloud matching to perform prostate alignment. Biomechanical modeling was used to regularize the prostate deformation during image registration. The performance of the network was evaluated using dice similarity coefficient (DSC) mean surface distance (MSD) Hausdorff distance (HD) and target registration error (TRE) The calculated DSC, MSD, HD and TRE were 0.94+ 0.02, 0.90+ 0.23mm, 2.96+ 1.00mm and 1.57+ 0.77mm, respectively. Our results showed that the proposed method can register the prostate on MRI to that on the TRUS accurately