Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics

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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.
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.
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.
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.
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.
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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