1,721,090 research outputs found
Senseable spaces: from a theoretical perspective to the application in augmented environments
Full text linkGrazie all’ enorme diffusione di dispositivi senzienti nella vita di tutti i giorni, nell’ ultimo decennio abbiamo assistito ad un cambio definitivo nel modo in cui gli utenti interagiscono con lo spazio circostante.
Viene coniato il termine Spazio Sensibile, per descrivere quegli spazi in grado di fornire servizi contestuali agli utenti, misurando e analizzando le dinamiche che in esso avvengono, e di reagire conseguentemente a questo continuo flusso di dati bidirezionale.
La ricerca è stata condotta abbracciando diversi domini di applicazione, le cui singole esigenze hanno reso necessario testare il concetto di Spazi Sensibili in diverse declinazioni, mantenendo al centro della ricerca l’utente, con la duplice accezione di end-user e manager.
Molteplici sono i contributi rispetto allo stato dell’ arte. Il concetto di Spazio Sensibile è stato calato nel settore dei Beni Culturali, degli Spazi Pubblici, delle Geosciences e del Retail. I casi studio nei musei e nella archeologia dimostrano come l’ utilizzo della Realtà Aumentata possa essere sfruttata di fronte a un dipinto o in outdoor per la visualizzazione di modelli complessi, In ambito urbano, il monitoraggio di dati generati dagli utenti ha consentito di capire le dinamiche di un evento di massa, durante il quale le stesse persone fruivano di servizi contestuali. Una innovativa applicazione di Realtà Aumentata è stata come servizio per facilitare l’ ispezione di fasce tampone lungo i fiumi, standardizzando flussi di dati e modelli provenienti da un Sistema Informativo Territoriale. Infine, un robusto sistema di indoor localization è stato istallato in ambiente retail, per scopi classificazione dei percorsi e per determinare le potenzialità di un punto vendita.
La tesi è inoltre una dimostrazione di come Space Sensing e Geomstica siano discipline complementari: la geomatica consente di acquisire e misurare dati geo spaziali e spazio temporali a diversa scala, lo Space Sensing utilizza questi dati per fornire servizi all’ utente precisi e contestuali
DISCOVERING AND SHARING OF SECRET ARCHITECTURES: THE HIDDEN TOMB OF THE PHARAOH OF EL-KHASNEH, JORDAN
Full text linkThe documentation of the archaeological heritage through 3D models to know ancient findings, has become a common practice within
the international panorama. Using minimal hardware, as well as its ease of use in almost every environmental condition, make 3D sampling
solutions based on Multiple View Stereo (MVS) matching and Structure from Motion techniques ideal for on-site documentation
of excavations or emergencies. Moreover, the availability of inexpensive platforms for web-based visualization represents great benefit
in the field of archaeology, where generally the low budged and the limitation of more complex instruments are a must. The case study
presented in these pages, experienced in Petra, Jordan, moves towards this direction. In the close proximity of the El- Khasneh façade,
is situated an excavation where two entrance, well preserved, give access to the Tomb of Pharaoh. The documentation described in these
pages has the twofold objective of providing the research community with a priceless dataset, acquired for one of the most important
heritage of the world that is partially still unknown and to share on line these computations. This work confirms how cultural heritage
documentation and dissemination of architectural rests, that are important for tourism and their interactive visualization, can strongly
benefit from the creation of 3D models and their sharing on the web. This particular archaeological setting is an interesting base for
investigation, given the complexity of the structure and its precarious condition
Il ruolo della geomatica per la conservazione del patrimonio culturale attraverso sistemi informativi dedicati
No full textThis contribution emphasizes the significant role of geomatics in managing risks and conserving built cultural heritage. It discusses challenges and opportunities in managing diverse information through geomatic techni-ques, which enable data acquisition, processing, and the creation of detailed models. The integration of these models into information systems like BIM enhances their management, with the use of semantic ontology for handling multidisciplinary aspects. The establishment of standards promotes interoperability, providing a com-prehensive view. The text also addresses conceptual and technical challenges in semantic definition within BIM and suggests solutions. The adoption of a cloud environment facilitates sharing information models, contribu-ting to collaborative, inclusive, and sustainable heritage management. A case study is presented, focusing on re-search activities related to restoring the defensive walls of a village of Central Italy damaged by 2016 earthquak
Digitalizzazione di Precisione del Patrimonio Culturale: il caso studio di Porta Pia, Ancona
No full textQuesto studio si concentra sulle più recenti innovazioni per l’acquisizione e la digitalizzazione del patrimonio culturale (DCH) tangibile, con l'obiettivo di contribuire alla definizione di procedure standard, definiti da protocolli internazionali, per l'acquisizione e la valutazione dei dati, sostenendo la conservazione, il restauro e la valorizzazione dei beni culturali. Il contributo si basa sul confronto di diverse tecnologie LiDAR per la rappresentazione 3D di un bene culturale, con particolare attenzione al livello di accuratezza e dettaglio ottenuti. È stato individuato come caso studio Porta Pia, un esempio di architettura barocca della città di Ancona. A tale scopo, sono stati impiegati tre diversi sensori di acquisizione dati: il MMS FARO Orbis e i TLS FARO FOCUS Premium e REALSEE Galois M2. Questi strumenti sono stati valutati per la loro versatilità e le modalità operative, evidenziando vantaggi e svantaggi in termini qualitativi e quantitativi, inclusi l'accuratezza metrica, la densità della nuvola di punti, i costi e i tempi di acquisizione ed elaborazione dei dati
RenderGAN: Enhancing Real-time Rendering Efficiency with Deep Learning
Full text linkIn the domain of computer graphics, achieving high visual quality in real-time rendering remains a formidable challenge due to the inherent time-quality tradeoff. Conventional real-time rendering engines sacrifice visual fidelity for interactive performance, while image generation using path-tracing techniques can be exceedingly time-consuming. In this article, we introduce RenderGAN, a deep learning-based solution designed to address this critical challenge in real-time rendering. RenderGAN uses G-Buffers and information from a real-time rendering engine as inputs to produce output images with exceptional visual fidelity. Its encoder-decoder architecture, trained using the Generative Adversarial Network (GAN) framework with perceptual loss, enhances image realism. To evaluate RenderGAN's effectiveness, we quantitatively compare the generated images with those of a path-tracing engine, obtaining a remarkable Universal Image Quality Index (UIQI) value of 0.898. RenderGAN's open source nature fosters collaboration, driving advancements in real-time computer graphics and rendering techniques. By bridging the gap between real-time and path-tracing rendering, RenderGAN opens new horizons for accelerated image generation, inspiring innovation and unlocking the full potential of real-time visual experiences. Project page: https://github.com/marcomameli1992/RenderNe
An automated workflow based on UAV imagery and Deep Learning methods for monitoring excavation area work
No full textThe rapid advancement of Artificial Intelligence (AI) is transforming the construction sector, particularly in site monitoring and safety management. Real-time monitoring enables the automatic detection of work progress issues, anomalies, and hazardous situations. However, no existing Deep Learning (DL)-based system is specifically designed to utilize Unmanned Aerial Vehicles (UAVs) for excavation area monitoring. This study presents an automated workflow that integrates UAV imagery with DL architectures, featuring a 1D Convolutional Neural Network (1D-CNN) for classifying excavation work phases and a VGG16 network for detecting safety fences. These technologies are incorporated into a Decision Support System (DSS), which automates report generation and enhances decision-making by providing structured, data-driven insights. The system was validated in a real-world case study involving an oil and gas construction company, demonstrating its ability to streamline site management tasks and improve safety oversight. Compared to traditional monitoring methods, our approach leverages UAV technology and DL methodologies to provide higher accuracy, efficiency, and scalability in excavation site monitoring. This contribution supports the digital transformation of construction management, offering a practical and innovative solution for real-time progress tracking and compliance verification
From AI Based Object Detection Model to Grape Yield Mapping for Precision Agriculture Applications
No full textYield mapping in vineyards is crucial for agronomic and economic management, allowing for precision operations like pruning, harvesting, fertilization, irrigation, and soil management. This leads to optimized resource use, improved grape quality, and increased productivity. Traditional yield mapping relies on expensive grape harvesters, which can cause grape loss and are not suitable for manually harvested vineyards. This research proposes a low-cost framework combining hardware and computer vision to generate yield variability maps, which can be used to create management zones for precision agriculture applications. The Yolov8 obtained the best performance with an overall precision of 89%. This approach aims to reduce reliance on costly machinery, enhance data accuracy, and make precision agriculture more accessible and effective
VIRTUAL RECONSTRUCTION OF LOST ARCHITECTURES: FROM THE TLS SURVEY TO AR VISUALIZATION
No full textThe exploitation of high quality 3D models for dissemination of archaeological heritage is currently an investigated topic, although Mobile Augmented Reality platforms for historical architecture are not available, allowing to develop low-cost pipelines for effective contents. The paper presents a virtual anastylosis, starting from historical sources and from 3D model based on TLS survey. Several
efforts and outputs in augmented or immersive environments, exploiting this reconstruction, are discussed.
The work demonstrates the feasibility of a 3D reconstruction approach for complex architectural shapes starting from point clouds and its AR/VR exploitation, allowing the superimposition with archaeological evidences. Major contributions consist in the presentation and the discussion of a pipeline starting from the virtual model, to its simplification showing several outcomes, comparing also the supported data qualities and advantages/disadvantages due to MAR and VR limitations
Nuove applicazioni di Realtà Aumentata per il Learning by Interacting. La App Ducale: tre capolavori della Galleria Nazionale delle Marche
Full text linkL’ uso di tecnologie innovative e filiere di digitalizzazione sostenibili e speditive forniscono una base di conoscenze imprescindibile per la lettura e la comprensione dei manufatti e delle opere d’arte. L’articolo presenta un lavoro di ricerca su applicazioni mobili sviluppate secondo il paradigma del learning by interacting. I contenuti della applicazione, rilasciata da Università Politecnica delle Marche per la Galleria Nazionale delle Marche, oltre a fornire informazioni storiche validate scientificamente da storici dell’arte ed esperti della Galleria, provengono da acquisizioni di dipinti ad altissima definizione che costituiscono la base per tutte le funzionalità. Grazie al monitoraggio della user experience ed alla standardizzazione dei nuovi contenuti, la app Ducale, descritta nell’ articolo, risulta un applicativo performante e centrato sull’utente. Il lavoro avvia una riflessione multidisciplinare sul patrimonio culturale digitale e contribuisce a diffondere metodi e strumenti adattativi per la comunicazione dei beni, facilitandone l’adozione in una larga parte dei musei
3VR: Vice Versa Virtual Reality Algorithm to Track and Map User Experience
Full text linkThe understanding of how users interact with the virtual cultural heritage could provide digital curators valuable insights into user behaviors and also improve the overall user experience through the ability to observe and record interactions of virtual visitors. This article introduces the new User Behavior (UB) tracking algorithm that we developed investigating a salience of the Virtual Reality (VR) panoramic regions. The algorithm extracts the importance of Region of Interest (ROI) determining patterns of the visitors’ virtual movement and interest in combination with statistics of captured browser activity. The input of our algorithm is the virtual online interactive platform (Virtual Museum of the Civic Art Gallery of Ascoli Piceno in Italy) with 81 16,386 × 8,192 pixels panoramic images and several interactive features including maps, thumbnails, and menus. The software engine of the tracking model “Vice Versa” VR operates on inverse functions of all descriptive functions (descriptors), which are assigned particularly to each interactive feature such as viewing multimedia content and observing the panoramic environment. The tracking experiment was performed online and the web virtual museum key study collected behavior information from 171 visitors around the world. Collected data, multimedia and textual content, and the coordinates of the ROIs are then subjected to standard statistics operations to define common patterns of UBs. Thus, we have discovered that the ROIs are mostly mapped onto the artworks and it is possible to obtain patterns about the main interests of users. The developed tool offers a guideline for the panoramic tours design and the potential benefits for museums are to understand the public, verify the effectiveness of choices, and re-shape a cultural offer based on visitors’ needs. Exploiting this kind of user experience, our algorithm ensures relevant feedback during virtual visits and thus paves the way for further development of the recommender system
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