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Now showing 1 - 10 of 71
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    An Approach to the Decomposition of Solids with Voids via Morse Theory
    (The Eurographics Association, 2023) Pareja-Corcho, Juan; Montoya-Zapata, Diego; Moreno, Aitor; Cadavid, Carlos; Posada, Jorge; Arenas-Tobon, Ketzare; Ruiz-Salguero, Oscar; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, Gilda
    The decomposition of solids is a problem of interest in areas of engineering such as feature recognition or manufacturing planning. The problem can be stated as finding a set of smaller and simpler pieces that glued together amount to the initial solid. This decomposition can be guided by geometrical or topological criteria and be applied to either surfaces or solids (embedded manifolds). Most topological decompositions rely on Morse theory to identify changes in the topology of a manifold. A Morse function f is defined on the manifold and the manifold's topology is studied by studying the behaviour of the critical points of f . A popular structure used to encode this behaviour is the Reeb graph. Reeb graph-based decompositions have proven to work well for surfaces and for solids without inner voids, but fail to consider solids with inner voids. In this work we present a methodology based on the handle-decomposition of a manifold that can encode changes in the topology of solids both with and without inner voids. Our methodology uses the Boundary Representation of the solid and a shape similarity criteria to identify changes in the topology of both the outer and inner boundary(ies) of the solid. Our methodology is defined for Morse functions that produce parallel planar level sets and we do not consider the case of annidated solids (i.e. solids within other solids). We present an algorithm to implement our methodology and execute experiments on several datasets. Future work includes the testing of the methodology with functions different to the height function and the speed up of the algorithm's data structure.
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    Extracting Ordered Iso-Geodesic Points on the Mesh
    (The Eurographics Association, 2020) Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    The mesh manifold is one of the most used modalities for representing 3D objects. Although it provides a fully connected not oriented structure, it has some drawback when compared to the grid of pixels of a still image. Indeed, mesh manifolds do not hold any order information, neither locally nor globally, which makes some operation computationally expensive or even impossible. To unleash its potential and to benefit from its capability of representing the 3D information, further advancements have to be made in order to allow basic operations (i.e., convolution) and effective descriptor extraction. In this paper, we present our preliminary study on a new approach to extract Iso-Geodesic points on a mesh manifold. The approach can be applied in various applications, from feature extraction, to convolution operation and mesh reconstruction. It also revealed to be robust to variations of mesh surface and tessellation, providing an effective geodesic distance approximation.
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    Yocto/GL: A Data-Oriented Library For Physically-Based Graphics
    (The Eurographics Association, 2019) Pellacini, Fabio; Nazzaro, Giacomo; Carra, Edoardo; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    In this paper we present Yocto/GL, a software library for computer graphics research and education. The library is written in C++ and targets execution on the CPU, with support for basic math, geometry and imaging utilities, path tracing and file IO. What distinguishes Yocto/GL from other similar projects is its minimalistic design and data-oriented programming style, which makes the library readable, extendible, and efficient. We developed Yocto/GL to meet our need, as a research group, of a simple and reliable codebase that lets us experiment with ease on research projects of various kind. After many iterations carried out over a few years, we settled on a design that we find effective for our purposes. In the hope of making our efforts valuable for the community, we share our experience in the development and make the library publicly available.
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    An Interactive Tuning Method for Generator Networks Trained by GAN
    (The Eurographics Association, 2022) Zhou, Mengyuan; Yamaguchi, Yasushi; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo
    The recent studies on GAN achieved impressive results in image synthesis. However, they are still not so perfect that output images may contain unnatural regions. We propose a tuning method for generator networks trained by GAN to improve their results by interactively removing unexpected objects and textures or changing the object colors. Our method could find and ablate those units in the generator networks that are highly related to the specific regions or their colors. Compared to the related studies, our proposed method can tune pre-trained generator networks without relying on any additional information like segmentation-based networks. We built the interactive system based on our method, capable of tuning the generator networks to make the resulting images as expected. The experiments show that our method could remove only unexpected objects and textures. It could change the selected area color as well. The method also gives us some hints to discuss the properties of generator networks which layers and units are associated with objects, textures, or colors.
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    HT-based Recognition of Patterns on 3D Shapes Using a Dictionary of Mathematical Curves
    (The Eurographics Association, 2019) Romanengo, Chiara; Biasotti, Silvia; FALCIDIENO, BIANCA; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    Characteristic curves play a fundamental role in the way a shape is perceived and illustrated. To address the curve recognition problem on surfaces, we adopt a generalisation of the Hough Transform (HT) which is able to deal with mathematical curves. In particular, we extend the set of curves so far adopted for curve recognition with the HT and propose a new dictionary of curves to be selected as templates. In addition, we introduce rules of composition and aggregation of curves into patterns, not limiting the recognition to a single curve at a time. Our method recognises various curves and patterns, possibly compound on a 3D surface. It selects the most suitable profile in a family of curves and, deriving from the HT, it is robust to noise and able to deal with data incompleteness. The system we have implemented is open and allows new additions of curves in the dictionary of functions already available.
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    Spectral-based Segmentation for Functional Shape-matching
    (The Eurographics Association, 2023) Mancinelli, Claudio; Melzi, Simone; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, Gilda
    In Computer Graphics and Computer Vision, shape co-segmentation and shape-matching are fundamental tasks with diverse applications, from statistical shape analysis to human-robot interaction. These problems respectively target establishing segmentto- segment and point-to-point correspondences between shapes, which are crucial task for numerous practical scenarios. Notably, co-segmentation can aid in point-wise correspondence estimation in shape-matching pipelines like the functional maps framework. Our paper introduces an innovative shape segmentation pipeline which provides coherent segmentation for shapes within the same class. Through comprehensive evaluation on a diverse test set comprising shapes from various datasets and classes, we demonstrate the coherence of our segmentation approach. Moreover, our method significantly improves accuracy in shape matching scenarios, as evidenced by comparisons with the original functional maps approach. Importantly, these enhancements come with minimal computational overhead. Our work not only introduces a novel coherent segmentation method and a valuable tool for improving correspondence accuracy within functional maps, but also contributes to the theoretical foundations of this impactful field, inspiring further research.
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    Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios
    (The Eurographics Association, 2021) Siddique, Arslan; Corsini, Massimiliano; Ganovelli, Fabio; Cignoni, Paolo; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Point cloud registration is a fundamental task in 3D reconstruction and environment perception. We explore the performance of modern Deep Learning-based registration techniques, in particular Deep Global Registration (DGR) and Learning Multiview Registration (LMVR), on an outdoor real world data consisting of thousands of range maps of a building acquired by a Velodyne LIDAR mounted on a drone. We used these pairwise registration methods in a sequential pipeline to obtain an initial rough registration. The output of this pipeline can be further globally refined. This simple registration pipeline allow us to assess if these modern methods are able to deal with this low quality data. Our experiments demonstrated that, despite some design choices adopted to take into account the peculiarities of the data, more work is required to improve the results of the registration.
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    Creating Adaptive and Interactive Stories in Mixed Reality
    (The Eurographics Association, 2022) Frau, Vittoria; Serra, Sergio; Spano, Lucio Davide; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo
    The following paper proposes the study, the design and the preliminary development of a solution for supporting users without programming experience in creating stories in a Mixed Reality environment. We focus on a Mixed Reality interface split into two parts: the creation and observation phases. During the creation phase, the end user can build his/her own story in the immersive mode of the Mixed Reality experience. The user can also enjoy the stories that other users have designed by seeing the characters appear in their surrounding environment.
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    IMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysis
    (The Eurographics Association, 2021) Sreevalsan-Nair, Jaya; Mohapatra, Pragyan; Singh, Satendra; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Both geometric and semantic information are required for a complete understanding of regions acquired as three-dimensional (3D) point clouds using the Light Detection and Ranging (LiDAR) technology. However, the global descriptors of such datasets that integrate both the information types are rare. With a focus on airborne LiDAR point clouds, we propose a novel global descriptor that transforms the point cloud from Cartesian to barycentric coordinate spaces. We use both the probabilistic geometric classification, aggregated from multiple scales, and the semantic classification to construct our descriptor using point rendering. Thus, we get an image-based multiscale global descriptor, IMGD. To demonstrate its usability, we propose the use of distribution distance measures between the descriptors for comparing the point clouds. Our experimental results demonstrate the effectiveness of our descriptor, when constructed of publicly available datasets, and on applying our selected distance measures.
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    A Geometric Approach for Computing the Kernel of a Polyhedron
    (The Eurographics Association, 2021) Sorgente, Tommaso; Biasotti, Silvia; Spagnuolo, Michela; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We present a geometric algorithm to compute the geometric kernel of a generic polyhedron. The geometric kernel (or simply kernel) is defined as the set of points from which the whole polyhedron is visible. Whilst the computation of the kernel for a polygon has already been largely addressed in the literature, less has been done for polyhedra. Currently, the principal implementation of the kernel estimation is based on the solution of a linear programming problem. We compare against it on several examples, showing that our method is more efficient in analysing the elements of a generic tessellation. Details on the technical implementation and discussions on pros and cons of the method are also provided.