Dr. Dario Billi
Ingegneria edile architettura, University of Pisa, Pisa, Tuscany, Italy
+1
Nobody said it was easy
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
4Years (2023-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications
Journal Article
Valeria CroceDario BilliGCGabriella Caroti
Gabriella Caroti
Andrea PiemonteLLLivio De Luca
Livio De Luca
Philippe VERONThis paper conducts a comparative evaluation between Neural Radiance Fields (NeRF) and photogrammetry for 3D reconstruction in the cultural heritage domain. Focusing on three case studies, of which the Terpsichore statue serves as a pilot case, the research assesses the quality, consistency, and efficiency of both methods. The results indicate that, under conditions of reduced input data or lower resolution, NeRF outperforms photogrammetry in preserving completeness and material description for the same set of input images (with known camera poses). The study recommends NeRF for scenarios requiring extensive area mapping with limited images, particularly in emergency situations. Despite NeRF’s developmental stage compared to photogrammetry, the findings demonstrate higher potential for describing material characteristics and rendering homogeneous textures with enhanced visual fidelity and accuracy; however, NeRF seems more prone to noise effects. The paper advocates for the future integration of NeRF with photogrammetry to address respective limitations, offering more comprehensive representation for cultural heritage preservation tasks. Future developments include extending applications to planar surfaces and exploring NeRF in virtual and augmented reality, as well as studying NeRF evolution in line with emerging trends in semantic segmentation and in-the-wild scene reconstruction.
1
Journal Article
Dario BilliValeria CroceMBMarco Giorgio Bevilacqua
Marco Giorgio BevilacquaGCGabriella Caroti
Gabriella CarotiAPAgnese Pasqualetti
Agnese Pasqualetti
Michele Russo +1
The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.
1
14 February 2024 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Journal Article
Valeria CroceGFG. Forleo
G. Forleo
Dario BilliMBMarco Giorgio Bevilacqua
Marco Giorgio BevilacquaAndrea PiemonteGCGabriella Caroti
Gabriella CarotiThis research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts.
Journal Article
This research investigates the metric accuracy of 3D transparent object reconstruction, a task where conventional photogrammetry often fails. The topic is especially relevant in cultural heritage (CH), where accurate digital documentation of glass and transparent artifacts is important. The work proposes a practical methodology using existing tools to verify metric accuracy standards. The study compares three methods, conventional photogrammetry, 3D Gaussian splatting (3DGS), and 2D Gaussian splatting (2DGS), to assess their ability to produce complete and metrically reliable 3D models suitable for measurement and geometric analysis. A transparent glass artifact serves as the case study. Results show that 2DGS captures fine surface and internal details with better geometric consistency than 3DGS and photogrammetry. Although 3DGS offers high visual quality, it introduces surface artifacts that affect metric reliability. Photogrammetry fails to reconstruct the object entirely. The study highlights that visual quality does not ensure geometric accuracy, which is critical for measurement applications. In this work, ground truth comparisons confirm that 2DGS offers the best trade-off between accuracy and appearance, despite higher computational demands. These findings suggest extending the experimentation to other sets of images featuring transparent objects, and possibly also reflective ones.
7 July 2025 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Journal Article
Dario BilliValeria CroceAndrea PiemonteGCGabriella Caroti
Gabriella CarotiUnderwater cultural heritage documentation presents significant challenges due to optical distortions inherent to aquatic environments. This study presents a dual approach for 3D reconstruction of underwater environments using two image-based 3D reconstruction techniques: Structure from Motion (SfM) and 3D Gaussian Splatting (3DGS). The goal is to evaluate the respective efficacy of each in terms of geometric accuracy and texture fidelity. The comparison is performed on the pilot case study of the Melania shipwreck, off the coast of Vada in Livorno, Italy. The results demonstrate that, while SfM proves more effective in structural reconstruction of submerged remains, 3DGS might enhance texture photo-realism and more effectively mitigate underwater lighting distortions. Conducted within the framework of the Sub-IA project, this research contributes to the development of integrated methodologies for the high-fidelity and realistic, image-based digital documentation of underwater heritage.
Preprint
This study evaluates three advanced approaches for 3D reconstruction of challenging transparent surfaces: conventional photogrammetry, enhanced 3D Gaussian Splatting, and novel 2D Gaussian Splatting (2DGS). Through a detailed case study of a glass artifact, the research demonstrates 2DGS's superior performance in geometric reconstruction and multi-view consistency, leveraging its innovative planar representation to outperform alternative methods in capturing fine surface details and complex internal structures. While the enhanced 3D approach shows advantages in visual rendering quality, it exhibits surface artifacts, and traditional photogrammetry proves inadequate for complete reconstruction. The comparative analysis highlights 2DGS's balanced capabilities in structural accuracy and perceptual quality, albeit with higher computational demands. These findings establish 2DGS as a significant advancement for cultural heritage documentation, particularly for transparent and reflective objects requiring precise digital preservation. The study identifies key directions for future development, including performance optimization and accessibility improvements, to facilitate broader adoption in heritage conservation and remote sensing applications where accurate 3D documentation is essential.
About Me
Dario Billi was born in Pisa on April 20, 1997, since he was a child he loved both graphic and abstract technical drawing.
After graduating from the E.Santoni technical institute for surveyors in Pisa in 2016 with a vote of 100/100, the principal commissioned him to create a mural work inside the school.
On the occasion of the Diploma Thesis he was first awarded for an urban planning competition of the Porta a Mare Urban Park, and the following year the project was also included and exhibited at the Pisa Architecture Biennale in the Bastion of San Gallo pavilion.
In 2017 he began his university academic career at the University of Pisa, at the Royal School of Engineering Faculty of Building Engineering-Architecture (single-cycle master's degree with limited enrollment), also becoming part of tutoring and co-teaching activities in the 4th year in the teaching subject Architectural Design I.
Subsequently he implements his skills regarding the world of UAS / RPAS survey photogrammetry also obtaining the A1 and A3 European drone license.
On November 17, 2022 he graduated from the Faculty of Building Engineering-Architecture with 110/110 cum laude, bringing a thesis concerning the application of artificial intelligence to assist photogrammetry processes.
Starting from January 2023 he obtained a scholarship at the Faculty of Engineering concerning the in-depth study of NeRF (Neural Radiance Field).