93 research outputs found

    µMatch: 3D shape correspondence for biological image data

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    Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the ÎĽMatch 3D shape correspondence pipeline. ÎĽMatch implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, ÎĽMatch does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of ÎĽMatch relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking

    MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

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    When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effective for parameterized geometries, practical scenarios often involve the absence of shape parametrization during the inference stage, leaving us with only mesh discretizations as available data. Learning simulations from such mesh-based representations poses significant challenges, with recent advances relying heavily on deep graph neural networks to overcome the limitations of conventional machine learning approaches. Despite their promising results, graph neural networks exhibit certain drawbacks, including their dependency on extensive datasets and limitations in providing built-in predictive uncertainties or handling large meshes. In this work, we propose a machine learning method that do not rely on graph neural networks. Complex geometrical shapes and variations with fixed topology are dealt with using well-known mesh morphing onto a common support, combined with classical dimensionality reduction techniques and Gaussian processes. The proposed methodology can easily deal with large meshes without the need for explicit shape parameterization and provides crucial predictive uncertainties, which are essential for informed decision-making. In the considered numerical experiments, the proposed method is competitive with respect to existing graph neural networks, regarding training efficiency and accuracy of the predictions

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    GLASS: Geometric Latent Augmentation for Shape Spaces

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    We investigate the problem of training generative models on very sparse collections of 3D models. Particularly, instead of using difficult-to-obtain large sets of 3D models, we demonstrate that geometrically-motivated energy functions can be used to effectively augment and boost only a sparse collection of example (training) models. Technically, we analyze the Hessian of the as-rigid-as-possible (ARAP) energy to adaptively sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process, of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. We evaluate our method against a set of strong baselines, provide ablation studies, and demonstrate application towards establishing shape correspondences. Glassproduces multiple interesting and meaningful shape variations even when starting from as few as 3-10 training shapes. Our code is available at https://sanjeevmk.github.io/glass_webpage/

    FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields

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    This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs), without using intermediate representations such as 2D images, depth maps, or point clouds. Our 3D coarse-to-fine Face Landmarks NeRF (FLNeRF) model efficiently samples from the NeRF on the whole face with individual facial features for accurate landmarks. To mitigate the limited number of facial expressions in the available data, local and non-linear NeRF warp is applied at facial features in fine scale to simulate large emotions range, including exaggerated facial expressions (e.g., cheek blowing, wide opening mouth, eye blinking), for training FLNeRF. With such expression augmentation, our model can predict 3D landmarks not limited to the 20 discrete expressions given in the data. Robust 3D NeRF facial landmarks contribute to many downstream tasks. As an example, we modify MoFaNeRF to enable high-quality face editing and swapping using face landmarks on NeRF, allowing more direct control and wider range of complex expressions. Experiments show that the improved model using landmarks achieves comparable to better results.Comment: Hao Zhang and Tianyuan Dai contributed equally. Project website: https://github.com/ZHANG1023/FLNeR

    Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

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    The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

    A Framework for the Semantics-aware Modelling of Objects

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    The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. We approach the problem by considering semantics as the formalised knowledge related to a category of objects; the geometry can vary provided that the semantics is preserved. We represent the semantics and the variable geometry of a class of shapes through the parametric template: an annotated 3D model whose geometry can be deformed provided that some semantic constraints remain satisfied. In this work, we design and develop a framework for the semantics-aware modelling of shapes, offering the user a single application environment where the whole workflow of defining the parametric template and applying semantics-aware deformations can take place. In particular, the system provides tools for the selection and annotation of geometry based on a formalised contextual knowledge; shape analysis methods to derive new knowledge implicitly encoded in the geometry, and possibly enrich the given semantics; a set of constraints that the user can apply to salient parts and a deformation operation that takes into account the semantic constraints and provides an optimal solution. The framework is modular so that new tools can be continuously added. While producing some innovative results in specific areas, the goal of this work is the development of a comprehensive framework combining state of the art techniques and new algorithms, thus enabling the user to conceptualise her/his knowledge and model geometric shapes. The original contributions regard the formalisation of the concept of annotation, with attached properties, and of the relations between significant parts of objects; a new technique for guaranteeing the persistence of annotations after significant changes in shape's resolution; the exploitation of shape descriptors for the extraction of quantitative information and the assessment of shape variability within a class; and the extension of the popular cage-based deformation techniques to include constraints on the allowed displacement of vertices. In this thesis, we report the design and development of the framework as well as results in two application scenarios, namely product design and archaeological reconstruction

    Interpreting parametric-biomimicry design from cad Ń‚o bim software: digital modelling based on a sketch of nandi flame

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    This research represents an application of two digital modelling softwares, first digital modelling software, chosen as representative of Computer-Aided Design – CAD modelling tool was Fusion 360. The representative of Building Information Modelling (BIM) as second digital modelling software was ArchiCAD. The aim of the research was to translate the same parametric-biomimicry design methodology used in CAD process modelling into BIM environment. African species Spathodea campanulata P. Beauv, whose common name in Kenya is Nandi flame, has been selected for the purpose of this digital modelling processes. As one of the most spectacular flowering plants, Nandi flame is indigenous to the tropical dry forests in Kenya. The decorative flower of this species was the basic model, more precisely the botanical sketches of the flower. This sketches were implemented into digital modelling softwares and used for parametric modelling. The results of this processes were represented as urban models or installations (landscape-architectural elements) in open space. This approach of digitally generating conceptual solutions from nature elements has capability to boost the formulation of new creative inventions in the different fields. The unique geometric patterns found in the flower of Spathodea campanulata P. Beauv served as a good example of how we may transform these ideas into actual design installations– using CAD or BIM software tools. This research has been carried out with the aim to find the position of BIM tools in parametric biomimicry design
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