Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633100

Browse

Now showing 1 - 20 of 22

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.

Browse

Browsing Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics by Title

Results Per Page

Sort Options