Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics

Permanent URI for this collection

Online Event, October 28 – 29, 2021
Geometry
Straightedge and Compass Constructions on Surfaces
Claudio Mancinelli and Enrico Puppo
A Geometric Approach for Computing the Kernel of a Polyhedron
Tommaso Sorgente, Silvia Biasotti, and Michela Spagnuolo
Reposing and Retargeting Unrigged Characters with Intrinsic-extrinsic Transfer
Pietro Musoni, Riccardo Marin, Simone Melzi, and Umberto Castellani
LengthNet: Length Learning for Planar Euclidean Curves
Barak Or and Ido Amos
Modeling, Reconstruction, and Applications
3D Modeling and Integration of Heterogeneous Geo-data
Marianna Miola, Daniela Cabiddu, Simone Pittaluga, Michela Mortara, Marino Vetuschi Zuccolini, and Gianmario Imitazione
3D City Reconstruction from OpenStreetMap Data
Sara Kaszuba and Fabio Pellacini
IMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysis
Jaya Sreevalsan-Nair, Pragyan Mohapatra, and Satendra Singh
SlowDeepFood: a Food Computing Framework for Regional Gastronomy
Nauman Ullah Gilal, Khaled Al-Thelaya, Jens Schneider, James She, and Marco Agus
Short Papers 1: Rendering and Visualization
Guiding Lens-based Exploration using Annotation Graphs
Moonisa Ahsan, Fabio Marton, Ruggero Pintus, and Enrico Gobbetti
Mesh Colours for Gradient Meshes
Sarah D. Baksteen, Gerben J. Hettinga, Jose Echevarria, and Jiri Kosinka
Remote Volume Rendering with a Decoupled, Ray-Traced Display Phase
Stefan Zellmann
A High Quality 3D Controller for Mobile and Desktop Web Applications
Daniele Fornari, Luigi Malomo, and Paolo Cignoni
Augmented and Virtual Reality
STRONGER: Simple TRajectory-based ONline GEsture Recognizer
Marco Emporio, Ariel Caputo, and Andrea Giachetti
Pen2VR: A Smart Pen Tool Interface for Wire Art Design in VR
Wanwan Li
Exploring Upper Limb Segmentation with Deep Learning for Augmented Virtuality
Monica Gruosso, Nicola Capece, and Ugo Erra
Visualization
Efficient Image Vectorisation Using Mesh Colours
Gerben Jan Hettinga, Jose Echevarria, and Jiri Kosinka
Visual Analysis of Popping in Progressive Visualization
Ethan Waterink, Jiri Kosinka, and Steffen Frey
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
Jingyi Shen and Han-Wei Shen
Short Papers 2: Miscellanea
Approximating Shapes with Standard and Custom 3D Printed LEGO Bricks
Filippo Andrea Fanni, Alberto Dal Bello, Simone Sbardellini, and Andrea Giachetti
ProtoSketchAR: Prototyping in Augmented Reality via Sketchings
Simone Arriu, Gianmarco Cherchi, and Lucio Davide Spano
Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios
Arslan Siddique, Massimiliano Corsini, Fabio Ganovelli, and Paolo Cignoni

BibTeX (Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics)
@inproceedings{
10.2312:stag.20211470,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
A Geometric Approach for Computing the Kernel of a Polyhedron}},
author = {
Sorgente, Tommaso
and
Biasotti, Silvia
and
Spagnuolo, Michela
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211470}
}
@inproceedings{
10.2312:stag.20211469,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Straightedge and Compass Constructions on Surfaces}},
author = {
Mancinelli, Claudio
and
Puppo, Enrico
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211469}
}
@inproceedings{
10.2312:stag.20211474,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
3D City Reconstruction from OpenStreetMap Data}},
author = {
Kaszuba, Sara
and
Pellacini, Fabio
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211474}
}
@inproceedings{
10.2312:stag.20211473,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
3D Modeling and Integration of Heterogeneous Geo-data}},
author = {
Miola, Marianna
and
Cabiddu, Daniela
and
Pittaluga, Simone
and
Mortara, Michela
and
Vetuschi Zuccolini, Marino
and
Imitazione, Gianmario
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211473}
}
@inproceedings{
10.2312:stag.20211471,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Reposing and Retargeting Unrigged Characters with Intrinsic-extrinsic Transfer}},
author = {
Musoni, Pietro
and
Marin, Riccardo
and
Melzi, Simone
and
Castellani, Umberto
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211471}
}
@inproceedings{
10.2312:stag.20211472,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
LengthNet: Length Learning for Planar Euclidean Curves}},
author = {
Or, Barak
and
Amos, Ido
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211472}
}
@inproceedings{
10.2312:stag.20211476,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
SlowDeepFood: a Food Computing Framework for Regional Gastronomy}},
author = {
Gilal, Nauman Ullah
and
Al-Thelaya, Khaled
and
Schneider, Jens
and
She, James
and
Agus, Marco
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211476}
}
@inproceedings{
10.2312:stag.20211475,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
IMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysis}},
author = {
Sreevalsan-Nair, Jaya
and
Mohapatra, Pragyan
and
Singh, Satendra
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211475}
}
@inproceedings{
10.2312:stag.20211478,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Mesh Colours for Gradient Meshes}},
author = {
Baksteen, Sarah D.
and
Hettinga, Gerben J.
and
Echevarria, Jose
and
Kosinka, Jiri
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211478}
}
@inproceedings{
10.2312:stag.20211481,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
STRONGER: Simple TRajectory-based ONline GEsture Recognizer}},
author = {
Emporio, Marco
and
Caputo, Ariel
and
Giachetti, Andrea
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211481}
}
@inproceedings{
10.2312:stag.20211480,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
A High Quality 3D Controller for Mobile and Desktop Web Applications}},
author = {
Fornari, Daniele
and
Malomo, Luigi
and
Cignoni, Paolo
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211480}
}
@inproceedings{
10.2312:stag.20211479,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Remote Volume Rendering with a Decoupled, Ray-Traced Display Phase}},
author = {
Zellmann, Stefan
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211479}
}
@inproceedings{
10.2312:stag.20211477,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Guiding Lens-based Exploration using Annotation Graphs}},
author = {
Ahsan, Moonisa
and
Marton, Fabio
and
Pintus, Ruggero
and
Gobbetti, Enrico
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211477}
}
@inproceedings{
10.2312:stag.20211483,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Exploring Upper Limb Segmentation with Deep Learning for Augmented Virtuality}},
author = {
Gruosso, Monica
and
Capece, Nicola
and
Erra, Ugo
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211483}
}
@inproceedings{
10.2312:stag.20211482,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Pen2VR: A Smart Pen Tool Interface for Wire Art Design in VR}},
author = {
Li, Wanwan
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211482}
}
@inproceedings{
10.2312:stag.20211484,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Efficient Image Vectorisation Using Mesh Colours}},
author = {
Hettinga, Gerben Jan
and
Echevarria, Jose
and
Kosinka, Jiri
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211484}
}
@inproceedings{
10.2312:stag.20211485,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Visual Analysis of Popping in Progressive Visualization}},
author = {
Waterink, Ethan
and
Kosinka, Jiri
and
Frey, Steffen
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211485}
}
@inproceedings{
10.2312:stag.20211486,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks}},
author = {
Shen, Jingyi
and
Shen, Han-Wei
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211486}
}
@inproceedings{
10.2312:stag.20211489,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios}},
author = {
Siddique, Arslan
and
Corsini, Massimiliano
and
Ganovelli, Fabio
and
Cignoni, Paolo
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211489}
}
@inproceedings{
10.2312:stag.20211487,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
Approximating Shapes with Standard and Custom 3D Printed LEGO Bricks}},
author = {
Fanni, Filippo Andrea
and
Dal Bello, Alberto
and
Sbardellini, Simone
and
Giachetti, Andrea
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211487}
}
@inproceedings{
10.2312:stag.20211488,
booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference},
editor = {
Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
}, title = {{
ProtoSketchAR: Prototyping in Augmented Reality via Sketchings}},
author = {
Arriu, Simone
and
Cherchi, Gianmarco
and
Spano, Lucio Davide
}, year = {
2021},
publisher = {
The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-165-6},
DOI = {
10.2312/stag.20211488}
}

Browse

Recent Submissions

Now showing 1 - 22 of 22
  • Item
    Smart Tools and Applications in computer Graphics: Frontmatter
    (The Eurographics Association, 2021) Frosini, Patrizio; Giorgi, Daniela; Melzi, Simone; Rodolà, Emanuele; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
  • Item
    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.
  • Item
    Straightedge and Compass Constructions on Surfaces
    (The Eurographics Association, 2021) Mancinelli, Claudio; Puppo, Enrico; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We discuss how classical straightedge and compass constructions can be ported to manifold surfaces under the geodesic metric. After defining the equivalent tools in the manifold domain, we analyze the most common constructions and show what happens when trying to port them to surfaces. Most such constructions fail, because the geometric properties on which they rely no longer hold under the geodesic metric. We devise some alternative constructions that guarantee at least some of the properties of their Euclidean counterpart; while we show that it is usually impossible to guarantee all properties together. Some constructions remain still unsolved, unless additional tools are used, which violate the constraints of the straightedge and compass framework since they take explicit distance measures. We integrate our constructions in the context of a prototype system that supports the interactive drawing of vector primitives on a surface represented with a high-resolution mesh.
  • Item
    3D City Reconstruction from OpenStreetMap Data
    (The Eurographics Association, 2021) Kaszuba, Sara; Pellacini, Fabio; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Virtual city generation from real data is far from being straightforward for users, as it strictly depends on the application domain, amount of information available, and the adopted reconstruction techniques. Nowadays, reconstruction of virtual cities is of interests in entertainment, urban planning, emergency response and machine learning. To serve these applications, we have developed an open-source tool that can reconstruct cities at scale directly from OpenStreetMap data, that can perform full city generation in the order of hundreds of seconds.
  • Item
    3D Modeling and Integration of Heterogeneous Geo-data
    (The Eurographics Association, 2021) Miola, Marianna; Cabiddu, Daniela; Pittaluga, Simone; Mortara, Michela; Vetuschi Zuccolini, Marino; Imitazione, Gianmario; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    This paper tackles the volumetric representation of geophysical and geotechnical data, gathered during exploration surveys of the subsoil. The creation of a 3D model as support to geological interpretation has to take into account the specificity of the diverse input data, that are heterogeneous. Some data are massive, but cover the domain unevenly, e.g., structured along dense differently spaced lines, while others are very sparse, e.g., borehole locations with soil sampling and CPTU (Piezocone Penetration Test) locations. In this work, we focus on the exploration and analysis of underwater deposits. After a discussion about the data typically acquired in an offshore campaign, we present an automatic process to generate the subsurfaces and volume defining an underground deposit, starting from the identification of relevant morphological features in seismic data. In particular, data simplification and refinement based on geostatistics have been applied to generate regular 2D meshes from strongly anisotropic data, in order to improve the quality of the final 3D tetrahedral mesh. Furthermore, we also use geostatistics to predict geotechnical parameters from local surveys and estimate their distribution on the whole domain: in this way the 3D model will include relevant geological features of the deposit and allow extrapolating different geotechnical information with associated uncertainty. The volume characterization and its 3D inspection will improve the structural and stratigraphic interpretation of deposits, to support geological analysis and planning of future engineering activities.
  • Item
    Reposing and Retargeting Unrigged Characters with Intrinsic-extrinsic Transfer
    (The Eurographics Association, 2021) Musoni, Pietro; Marin, Riccardo; Melzi, Simone; Castellani, Umberto; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    In the 3D digital world, deformations and animations of shapes are fundamental topics for several applications. The entertainment industry, virtual and augmented reality, human-robot interactions are just some examples that pay attention to animation processes and related tools. In these contexts, researchers from several communities desire to govern deformations and animations of 3D geometries. This task is generally very complicated because it requires several skills covering different kinds of knowledge. For this reason, we propose a ready-to-use procedure to transfer a given animation from a source shape to a target shape that shares the same global structure. Our method proposes highly geometrical transferring, reposing, and retargeting, providing high-quality and efficient transfer, as shown in the qualitative evaluation that we report in the experimental section. The animation transfer we provide will potentially impact different scenarios, such as data augmentation for learning-based procedures or virtual avatar generation for orthopedic rehabilitation and social applications.
  • Item
    LengthNet: Length Learning for Planar Euclidean Curves
    (The Eurographics Association, 2021) Or, Barak; Amos, Ido; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    In this work, we used a deep learning (DL) model to solve a fundamental problem in differential geometry. One can find many closed-form expressions for calculating curvature, length, and other geometric properties in the literature. As we know these properties, we are highly motivated to reconstruct them by using DL models. In this framework, our goal is to learn geometric properties from many examples. The simplest geometric object is a curve, and one of the fundamental properties is the length. Therefore, this work focuses on learning the length of planar sampled curves created by a simulation. The fundamental length axioms were reconstructed using a supervised learning approach. Following these axioms, a DL-based model, we named LengthNet, was established. For simplicity, we focus on the planar Euclidean curves.
  • Item
    SlowDeepFood: a Food Computing Framework for Regional Gastronomy
    (The Eurographics Association, 2021) Gilal, Nauman Ullah; Al-Thelaya, Khaled; Schneider, Jens; She, James; Agus, Marco; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Food computing recently emerged as a stand-alone research field, in which artificial intelligence, deep learning, and data science methodologies are applied to the various stages of food production pipelines. Food computing may help end-users in maintaining healthy and nutritious diets by alerting of high caloric dishes and/or dishes containing allergens. A backbone for such applications, and a major challenge, is the automated recognition of food by means of computer vision. It is therefore no surprise that researchers have compiled various food data sets and paired them with well-performing deep learning architecture to perform said automatic classification. However, local cuisines are tied to specific geographic origins and are woefully underrepresented in most existing data sets. This leads to a clear gap when it comes to food computing on regional and traditional dishes. While one might argue that standardized data sets of world cuisine cover the majority of applications, such a stance would neglect systematic biases in data collection. It would also be at odds with recent initiatives such as SlowFood, seeking to support local food traditions and to preserve local contributions to the global variation of food items. To help preserve such local influences, we thus present a full end-to-end food computing network that is able to: (i) create custom image data sets semi-automatically that represent traditional dishes; (ii) train custom classification models based on the EfficientNet family using transfer learning; (iii) deploy the resulting models in mobile applications for real-time inference of food images acquired through smart phone cameras. We not only assess the performance of the proposed deep learning architecture on standard food data sets (e.g., our model achieves 91:91% accuracy on ETH’'s Food-101), but also demonstrate the performance of our models on our own, custom data sets comprising local cuisine, such as the Pizza-Styles data set and GCC-30. The former comprises 14 categories of pizza styles, whereas the latter contains 30 Middle Eastern dishes from the Gulf Cooperation Council members.
  • Item
    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.
  • Item
    Mesh Colours for Gradient Meshes
    (The Eurographics Association, 2021) Baksteen, Sarah D.; Hettinga, Gerben J.; Echevarria, Jose; Kosinka, Jiri; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We present an extension of the popular gradient mesh vector graphics primitive with the addition of mesh colours, aiming to reduce the mesh complexity needed to describe intricate colour gradients and textures. We present interesting applications to user-guided authoring of detailed vector graphics and image vectorisation.
  • Item
    STRONGER: Simple TRajectory-based ONline GEsture Recognizer
    (The Eurographics Association, 2021) Emporio, Marco; Caputo, Ariel; Giachetti, Andrea; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    In this paper, we present STRONGER, a client-server solution for the online gesture recognition from captured hands' joints sequences. The system leverages a CNN-based recognizer improving current state-of-the-art solutions for segmented gestures classification, trained and tested for the online gesture recognition task on a recent benchmark including heterogeneous gestures. The recognizer provides good classification accuracy and a limited number of false positives on most of the gesture classes of the benchmark used and has been used to create a demo application in a Mixed Reality scenario using an Hololens 2 optical see through Head-Mounted Display with hand tracking capability.
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    A High Quality 3D Controller for Mobile and Desktop Web Applications
    (The Eurographics Association, 2021) Fornari, Daniele; Malomo, Luigi; Cignoni, Paolo; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    The interaction between a 2D input device (like a mouse or a touchscreen) and a 3D object on the screen with the purpose of examining it in detail is a well-studied interaction problem. The inherent difference in degrees of freedom between input devices and possible 3D transformations makes it difficult to intuitively map inputs to operations to be performed on 3D objects. Although, over the years, studies led to a wide variety of solutions to overcome this problem, most of them are not actually available in real-world applications. In particular, for 3D web applications, only basic solutions are often implemented, and even the most used web framework for 3D still lacks state of the art implementations. We will face the problem of 3D interaction through touch and mouse input, and we propose our implementation of a 3D view manipulator for web applications, which offers a natural control, advanced functionalities, and provides an easy-to-use interface for both desktop and mobile environments.
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    Remote Volume Rendering with a Decoupled, Ray-Traced Display Phase
    (The Eurographics Association, 2021) Zellmann, Stefan; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. For that we build on prior work where we sample the volume on the client using ray casting and reconstruct z-values based on heuristics. Color and depth buffers are then sent to the client, which reuses this depth image as a stand-in for subsequent frames by warping it to reflect the current camera position and orientation until new data was received from the server. The extension we propose in this work represents the depth pixels as spheres and ray traces them on the client side. In contrast to the reference method, this representation adapts the footprint of the depth pixels to the distance to the camera origin, which is more effective at hiding warping artifacts, particularly when applied to volumetric data sets.
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    Guiding Lens-based Exploration using Annotation Graphs
    (The Eurographics Association, 2021) Ahsan, Moonisa; Marton, Fabio; Pintus, Ruggero; Gobbetti, Enrico; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We introduce a novel approach for guiding users in the exploration of annotated 2D models using interactive visualization lenses. Information on the interesting areas of the model is encoded in an annotation graph generated at authoring time. Each graph node contains an annotation, in the form of a visual markup of the area of interest, as well as the optimal lens parameters that should be used to explore the annotated area and a scalar representing the annotation importance. Graph edges are used, instead, to represent preferred ordering relations in the presentation of annotations. A scalar associated to each edge determines the strength of this prescription. At run-time, the graph is exploited to assist users in their navigation by determining the next best annotation in the database and moving the lens towards it when the user releases interactive control. The selection is based on the current view and lens parameters, the graph content and structure, and the navigation history. This approach supports the seamless blending of an automatic tour of the data with interactive lens-based exploration. The approach is tested and discussed in the context of the exploration of multi-layer relightable models.
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    Exploring Upper Limb Segmentation with Deep Learning for Augmented Virtuality
    (The Eurographics Association, 2021) Gruosso, Monica; Capece, Nicola; Erra, Ugo; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Sense of presence, immersion, and body ownership are among the main challenges concerning Virtual Reality (VR) and freehand-based interaction methods. Through specific hand tracking devices, freehand-based methods can allow users to use their hands for VE interaction. To visualize and make easy the freehand methods, recent approaches take advantage of 3D meshes to represent the user's hands in VE. However, this can reduce user immersion due to their unnatural correspondence with the real hands. We propose an augmented virtuality (AV) pipeline allows users to visualize their limbs in VE to overcome this limit. In particular, they were captured by a single monocular RGB camera placed in an egocentric perspective, segmented using a deep convolutional neural network (CNN), and streamed in the VE. In addition, hands were tracked through a Leap Motion controller to allow user interaction. We introduced two case studies as a preliminary investigation for this approach. Finally, both quantitative and qualitative evaluations of the CNN results were provided and highlighted the effectiveness of the proposed CNN achieving remarkable results in several real-life unconstrained scenarios.
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    Pen2VR: A Smart Pen Tool Interface for Wire Art Design in VR
    (The Eurographics Association, 2021) Li, Wanwan; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    In this paper, we present Pen2VR: a smart pen tool interface for 3D drawing wire art design in VR. In Pen2VR, with VR headsets put on, users are allowed to create their VR drawings as 3D lines or curves in a virtual space as if they are using a 3D version of the pen tool in Photoshop. During the 3D drawing in VR, users can directly create geometric elements including polylines and Bezier curves by clicking and dragging a VR controller in mid-air. Besides, in order to make Pen2VR smart, we proposed an optimization-based approach to automatically fine-tune the user's 3D initial input into well-beautified 3D drawings. Experimental results show that the qualities of optimized wire art designs have been significantly improved.
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    Efficient Image Vectorisation Using Mesh Colours
    (The Eurographics Association, 2021) Hettinga, Gerben Jan; Echevarria, Jose; Kosinka, Jiri; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Image vectorisation methods proposed in the past have not seen wide adoption due to performance, quality, controllability, and/or generality issues.We present a vectorisation method that uses mesh colours as a vector primitive for image vectorisation. We show that mesh colours have clear benefits for rendering performance and texture detail. Due to their flexibility, they also enable a simplified and more efficient generation of meshes of curved triangular patches, which are in our case constrained by our image feature extraction algorithm. The proposed method follows a standard pipeline where each step is efficient and controllable, leading to results that compare favourably with those from previous work. We show results over a variety of input images including photos, drawings, paintings, designs, and cartoons and also devise a user-guided vectorisation variant.
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    Visual Analysis of Popping in Progressive Visualization
    (The Eurographics Association, 2021) Waterink, Ethan; Kosinka, Jiri; Frey, Steffen; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Progressive visualization allows users to examine intermediate results while they are further refined in the background. This makes them increasingly popular when dealing with large data and computationally expensive tasks. The characteristics of how preliminary visualizations evolve over time are crucial for efficient analysis; in particular unexpected disruptive changes between iterations can significantly hamper the user experience. This paper proposes a visualization framework to analyze the refinement behavior of progressive visualization. We particularly focus on sudden significant changes between the iterations, which we denote as popping artifacts, in reference to undesirable visual effects in the context of level of detail representations in computer graphics. Our visualization approach conveys where in image space and when during the refinement popping artifacts occur. It allows to compare across different runs of stochastic processes, and supports parameter studies for gaining further insights and tuning the algorithms under consideration. We demonstrate the application of our framework and its effectiveness via two diverse use cases with underlying stochastic processes: adaptive image space sampling, and the generation of grid layouts.
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    An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
    (The Eurographics Association, 2021) Shen, Jingyi; Shen, Han-Wei; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Despite the great success of Convolutional Neural Networks (CNNs) in Computer Vision and Natural Language Processing, the working mechanism behind CNNs is still under extensive discussion and research. Driven by strong demand for the theoretical explanation of neural networks, some researchers utilize information theory to provide insight into the black-box model. However, to the best of our knowledge, employing information theory to quantitatively analyze and qualitatively visualize neural networks has not been extensively studied in the visualization community. In this paper, we combine information entropies and visualization techniques to shed light on how CNN works. Specifically, we first introduce a data model to organize the data that can be extracted from CNN models. Then we propose two ways to calculate entropy under different circumstances. To provide a fundamental understanding of the basic building blocks of CNNs (e.g., convolutional layers, pooling layers, normalization layers) from an information-theoretic perspective, we develop a visual analysis system, CNNSlicer. CNNSlicer allows users to interactively explore the amount of information changes inside the model. With case studies on the widely used benchmark datasets (MNIST and CIFAR-10), we demonstrate the effectiveness of our system in opening the black-box of CNNs.
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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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    Approximating Shapes with Standard and Custom 3D Printed LEGO Bricks
    (The Eurographics Association, 2021) Fanni, Filippo Andrea; Dal Bello, Alberto; Sbardellini, Simone; Giachetti, Andrea; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    In this paper, we present a work-in-progress aimed at developing a pipeline for the fabrication of shapes reproducing digital models with a combination of standard LEGO bricks and 3D printed custom elements. The pipeline starts searching for the ideal alignment of the 3D model with the brick grid. It then employs a novel approach for shape "legolization" using a outside-in heuristic to limit critical configuration, and separates an external shell and an internal part. Finally, it exploits shape booleans to create the external custom parts to be 3D printed.
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    ProtoSketchAR: Prototyping in Augmented Reality via Sketchings
    (The Eurographics Association, 2021) Arriu, Simone; Cherchi, Gianmarco; Spano, Lucio Davide; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Prototyping is a widely used technique in the early stages of system design, and it is an essential part of a new product development process. During this phase, designers identify the main functionalities, concepts and contents of the system without creating a fully functional system. This paper aims to discuss the development of ProtoSketchAR, a tool enabling Augmented Reality (AR) prototyping by sketching. The application has different interaction modes, depending on the performed functionality. Basically, it is possible to create 2D/3D sketches to be placed in the real environment and to manipulate them. These functionalities allow the creation of virtual elements that can be used to prototype screens of AR applications. The application is web-based so that it can be run on any device with a compatible AR browser, regardless of the operating system used.