DIDYMOS-XR: Difference between revisions
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. |