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3D reconstruction and rendering for remote sensing images

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.

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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

FONDAZIONE BRUNO KESSLER
Net EU contribution

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.

€ 209 483,28
Address
VIA SANTA CROCE 77
38122 Trento
Italy

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Region
Nord-Est Provincia Autonoma di Trento Trento
Activity type
Research Organisations
Links
Total cost

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.

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