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Created page with "=== DIDYMOS-XR Project === {| class='wikitable' style='margin:auto' |- ! CORDIS Reference !! Start date !! End date !! Coordinator |- | https://cordis.europa.eu/project/id/101092875 || 01/01/2023 || 31/12/2025 || JOANNEUM / Austria |} === Project description === The digital transformation and the availability of more diversified and cost-effective means for 3D capture have led to the creation of digital twins also for physical environments. Based on such digital twin..."
 
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! CORDIS Reference !! Start date !! End date !! Coordinator
! CORDIS Reference !! Start date !! End date !! Coordinator !! Project website
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| https://cordis.europa.eu/project/id/101092875 || 01/01/2023 || 31/12/2025 || JOANNEUM  / Austria
| https://cordis.europa.eu/project/id/101092875 || 01/01/2023 || 31/12/2025 || JOANNEUM  / Austria || https://didymos-xr.eu/
|}  
|}  
=== Project description ===
=== Project description ===
The digital transformation and the availability of more diversified and cost-effective means for 3D capture have led to the creation of digital twins also for physical environments. Based on such digital twins, various applications could be built using real-time data from real-world environments, serving as a blueprint for smart cities and for improving performance and efficiency across industries. Currently, creating high-fidelity digital twins is costly, and their update requires manual intervention. Furthermore, data integration from heterogeneous sensors is challenging. The EU-funded DIDYMOS-XR project will implement technology to create improved large-scale digital twins, synchronised with the real world. DIDYMOS-XR will investigate and develop methods for data reconstruction and mapping from heterogeneous inputs, including static and mobile sensors, AI-based data fusion, scene understanding and rendering.
The digital transformation and the availability of more diversified and cost-effective means for 3D capture have led to the creation of digital twins also for physical environments. Based on such digital twins, various applications could be built using real-time data from real-world environments, serving as a blueprint for smart cities and for improving performance and efficiency across industries. Currently, creating high-fidelity digital twins is costly, and their update requires manual intervention. Furthermore, data integration from heterogeneous sensors is challenging. The EU-funded DIDYMOS-XR project will implement technology to create improved large-scale digital twins, synchronised with the real world. DIDYMOS-XR will investigate and develop methods for data reconstruction and mapping from heterogeneous inputs, including static and mobile sensors, AI-based data fusion, scene understanding and rendering.
=== Project outputs ===
==== Publications ====
{| class="wikitable sortable"
! Domain !! Type of output !! Title !! DOI URL
|-
| AI, Machine Learning & Data Science || Conference proceedings || Perception for Connected Autonomous Vehicles under Adverse Weather Conditions || https://doi.org/10.1109/IROS58592.2024.10801295
|-
| AI, Machine Learning & Data Science || Conference proceedings || User-Centric Evaluation Methods for Digital Twin Applications in Extended Reality || https://doi.org/10.1109/AIXVR63409.2025.00028
|-
| AI, Machine Learning & Data Science || Conference proceedings || MGSO: Monocular Real-Time Photometric SLAM with Efficient 3D Gaussian Splatting || https://doi.org/10.48550/ARXIV.2409.13055
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || HINT-3D: Human-in-the-Loop Interactive Test-Time Adaptation for 3D Segmentation || https://doi.org/10.5281/ZENODO.18491843
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || Deep 3D Geometric Saliency Estimation from Light Field Images || https://doi.org/10.1109/DSP58604.2023.10167953
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || HAME-NeRF: High Accuracy Mesh Extraction Leveraging Neural Radiance Fields || https://doi.org/10.1007/978-3-032-04968-1_28
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || Visual Localization in Complex Environments: Merging Traditional Geometry with Learning-Based Techniques || https://doi.org/10.1109/AIXVR63409.2025.00022
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || Volumetric Video Reconstruction and Communications: Toward a New Era of Interactive and Immersive Social Virtual Reality (VR) Experiences || https://doi.org/10.1145/3672406.3672421
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || Training a Segmentation-Based Visual Anonymization Service for Street Scenes || https://doi.org/10.1007/978-981-96-2074-6_26
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || GDNeRF: Generalizable Depth-based NeRF for sparse view synthesis || https://doi.org/10.1109/ICME59968.2025.11209482
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || HAL-NeRF: High Accuracy Localization Leveraging Neural Radiance Fields || https://doi.org/10.1109/AIXVR63409.2025.00024
|-
| Computer Vision, 3D Modeling & Rendering || Conference proceedings || Leveraging Anisotropic Error for Robust Point Cloud Registration || https://doi.org/10.1109/DSP65409.2025.11074865
|-
| Computer Vision, 3D Modeling & Rendering || Peer reviewed articles || Urban scene removal and completion || https://doi.org/10.3233/FAIA250603
|-
| Computer Vision, 3D Modeling & Rendering || Peer reviewed articles || Visual localization using implicit representations and particle filtering-based pose refinement || https://doi.org/10.5281/ZENODO.18468790
|-
| Computer Vision, 3D Modeling & Rendering || Peer reviewed articles || ExpPoint-MAE: Better Interpretability and Performance for Self-Supervised Point Cloud Transformers || https://doi.org/10.1109/ACCESS.2024.3388155
|-
| Computer Vision, 3D Modeling & Rendering || Peer reviewed articles || Interactive digital twins enabling responsible extended reality applications || https://doi.org/10.1038/S41598-025-17855-9
|-
| Computer Vision, 3D Modeling & Rendering || Peer reviewed articles || MaskUno: Switch-Split Block For Enhancing Instance Segmentation || https://doi.org/10.48550/ARXIV.2407.21498
|-
| Ethics, Society, Arts & Culture || Conference proceedings || Digital Twins for Extended Reality Tourism: User Experience Evaluation Across User Groups || https://doi.org/10.1007/978-3-031-97769-5_3
|-
| Ethics, Society, Arts & Culture || Peer reviewed articles || Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites || https://doi.org/10.3390/APP15052854
|-
| Extended Reality (VR/AR/MR) & HCI || Conference proceedings || Cooperative Perception for Digital Twin Reconstruction* || https://doi.org/10.1109/AIXVR63409.2025.00023
|}
==== Technological assets ====
{| class="wikitable sortable"
! Title !! Type of Asset !! Link / DOI !! Description
|-
| Dataset for Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins || Dataset || https://ieeexplore.ieee.org/document/10859207 || Data utilized for learning scene semantics intended for city-scale digital twins.
|-
| ADAPT JR-Sim2Real dataset || Dataset || https://zenodo.org/records/12805642 || Dataset associated with domain transfer for instance segmentations for AR scenes.
|-
| MGSO || Software / Algorithm || https://doi.org/10.48550/ARXIV.2409.13055 || A monocular real-time photometric SLAM algorithm utilizing efficient 3D Gaussian Splatting.
|-
| HINT-3D || Software / Framework || https://doi.org/10.5281/ZENODO.18491843 || A human-in-the-loop interactive test-time adaptation framework for 3D segmentation.
|-
| Visual localization using implicit representations || Software / Model || https://doi.org/10.5281/ZENODO.18468790 || Software for visual localization utilizing implicit representations and particle filtering-based pose refinement.
|}

Latest revision as of 12:53, 22 April 2026

DIDYMOS-XR Project

CORDIS Reference Start date End date Coordinator Project website
https://cordis.europa.eu/project/id/101092875 01/01/2023 31/12/2025 JOANNEUM / Austria https://didymos-xr.eu/

Project description

The digital transformation and the availability of more diversified and cost-effective means for 3D capture have led to the creation of digital twins also for physical environments. Based on such digital twins, various applications could be built using real-time data from real-world environments, serving as a blueprint for smart cities and for improving performance and efficiency across industries. Currently, creating high-fidelity digital twins is costly, and their update requires manual intervention. Furthermore, data integration from heterogeneous sensors is challenging. The EU-funded DIDYMOS-XR project will implement technology to create improved large-scale digital twins, synchronised with the real world. DIDYMOS-XR will investigate and develop methods for data reconstruction and mapping from heterogeneous inputs, including static and mobile sensors, AI-based data fusion, scene understanding and rendering.

Project outputs

Publications

Domain Type of output Title DOI URL
AI, Machine Learning & Data Science Conference proceedings Perception for Connected Autonomous Vehicles under Adverse Weather Conditions https://doi.org/10.1109/IROS58592.2024.10801295
AI, Machine Learning & Data Science Conference proceedings User-Centric Evaluation Methods for Digital Twin Applications in Extended Reality https://doi.org/10.1109/AIXVR63409.2025.00028
AI, Machine Learning & Data Science Conference proceedings MGSO: Monocular Real-Time Photometric SLAM with Efficient 3D Gaussian Splatting https://doi.org/10.48550/ARXIV.2409.13055
Computer Vision, 3D Modeling & Rendering Conference proceedings HINT-3D: Human-in-the-Loop Interactive Test-Time Adaptation for 3D Segmentation https://doi.org/10.5281/ZENODO.18491843
Computer Vision, 3D Modeling & Rendering Conference proceedings Deep 3D Geometric Saliency Estimation from Light Field Images https://doi.org/10.1109/DSP58604.2023.10167953
Computer Vision, 3D Modeling & Rendering Conference proceedings HAME-NeRF: High Accuracy Mesh Extraction Leveraging Neural Radiance Fields https://doi.org/10.1007/978-3-032-04968-1_28
Computer Vision, 3D Modeling & Rendering Conference proceedings Visual Localization in Complex Environments: Merging Traditional Geometry with Learning-Based Techniques https://doi.org/10.1109/AIXVR63409.2025.00022
Computer Vision, 3D Modeling & Rendering Conference proceedings Volumetric Video Reconstruction and Communications: Toward a New Era of Interactive and Immersive Social Virtual Reality (VR) Experiences https://doi.org/10.1145/3672406.3672421
Computer Vision, 3D Modeling & Rendering Conference proceedings Training a Segmentation-Based Visual Anonymization Service for Street Scenes https://doi.org/10.1007/978-981-96-2074-6_26
Computer Vision, 3D Modeling & Rendering Conference proceedings GDNeRF: Generalizable Depth-based NeRF for sparse view synthesis https://doi.org/10.1109/ICME59968.2025.11209482
Computer Vision, 3D Modeling & Rendering Conference proceedings HAL-NeRF: High Accuracy Localization Leveraging Neural Radiance Fields https://doi.org/10.1109/AIXVR63409.2025.00024
Computer Vision, 3D Modeling & Rendering Conference proceedings Leveraging Anisotropic Error for Robust Point Cloud Registration https://doi.org/10.1109/DSP65409.2025.11074865
Computer Vision, 3D Modeling & Rendering Peer reviewed articles Urban scene removal and completion https://doi.org/10.3233/FAIA250603
Computer Vision, 3D Modeling & Rendering Peer reviewed articles Visual localization using implicit representations and particle filtering-based pose refinement https://doi.org/10.5281/ZENODO.18468790
Computer Vision, 3D Modeling & Rendering Peer reviewed articles ExpPoint-MAE: Better Interpretability and Performance for Self-Supervised Point Cloud Transformers https://doi.org/10.1109/ACCESS.2024.3388155
Computer Vision, 3D Modeling & Rendering Peer reviewed articles Interactive digital twins enabling responsible extended reality applications https://doi.org/10.1038/S41598-025-17855-9
Computer Vision, 3D Modeling & Rendering Peer reviewed articles MaskUno: Switch-Split Block For Enhancing Instance Segmentation https://doi.org/10.48550/ARXIV.2407.21498
Ethics, Society, Arts & Culture Conference proceedings Digital Twins for Extended Reality Tourism: User Experience Evaluation Across User Groups https://doi.org/10.1007/978-3-031-97769-5_3
Ethics, Society, Arts & Culture Peer reviewed articles Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites https://doi.org/10.3390/APP15052854
Extended Reality (VR/AR/MR) & HCI Conference proceedings Cooperative Perception for Digital Twin Reconstruction* https://doi.org/10.1109/AIXVR63409.2025.00023

Technological assets

Title Type of Asset Link / DOI Description
Dataset for Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins Dataset https://ieeexplore.ieee.org/document/10859207 Data utilized for learning scene semantics intended for city-scale digital twins.
ADAPT JR-Sim2Real dataset Dataset https://zenodo.org/records/12805642 Dataset associated with domain transfer for instance segmentations for AR scenes.
MGSO Software / Algorithm https://doi.org/10.48550/ARXIV.2409.13055 A monocular real-time photometric SLAM algorithm utilizing efficient 3D Gaussian Splatting.
HINT-3D Software / Framework https://doi.org/10.5281/ZENODO.18491843 A human-in-the-loop interactive test-time adaptation framework for 3D segmentation.
Visual localization using implicit representations Software / Model https://doi.org/10.5281/ZENODO.18468790 Software for visual localization utilizing implicit representations and particle filtering-based pose refinement.