Project description
New 3D modelling framework supports immersive-experience city models
Creating accurate, photorealistic 3D models of cities from remote sensing data is still challenging despite significant technical advances. Emerging neural rendering techniques offer more immersive reconstructions than traditional methods. However, they struggle with computational demands and integrating real-world phenomena such as atmospheric effects and lighting conditions. With the support of the Marie Skłodowska-Curie Actions programme, the 3D-RTR project aims to develop a new reconstruction framework that explicitly models the complete imaging chain using four key physical parameters – light source, atmosphere, scene and camera – enabling more physically accurate and visually compelling 3D city models. Tested across diverse locations and made publicly accessible, the results could significantly advance smart city applications and digital twin development.
Objective
Realistic 3D reconstruction from multiple Remote Sensing (RS) images has been studied as a Multi-View Stereo process, in which 3D scenes are reconstructed progressively in terms of geometry and appearance. This method can introduce artifacts and compromise realism. Recently, a new framework in neural rendering, the Neural Radiance Field (NeRF) and its variants, e.g. 3D-GS, which model both geometry and appearance of a scene within a radiance field, has delivered a more immersive scene. However, as with any burgeoning technology, NeRF has encountered its share of challenges and limitations, particularly in terms of computational efficiency and incapacity of modeling many ubiquitous, real-world phenomena in uncontrolled images.
3D-RTR aims at building a new 3D reconstruction framework to address these issues and provide high-quality, immersive experience of 3D city models. To build it, a unified, explicit representation of the complete imaging chain, which models four key physical entities (i.e. the light source, the atmosphere, the scene and the camera), is proposed to describe the real-world phenomena in a more direct way. This unified imaging chain is further transformed into a differentiable, highly parallelized architecture designed for learning realistic 3D scenes from remote sensing images, with various optimization tactics employed to accelerate the learning process. 3D-RTR will be tested using remote sensing data acquired on various locations and make these reconstructed models accessible to the public online.
To ensure the successful implementation of these project goals I will conduct my research at the 3DOM research unit of FBK Trento, with the guidance of Prof. Fabio Remondino, a leading scholar. This project will offer new solution for smart city applications and digital twin generations, while also preparing me to become a tenured scholar and obtain advanced funding, strengthening my academic profile and allowing me to establish my own research group.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors optical sensors
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences mathematics applied mathematics mathematical model
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
38122 Trento
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.