DIDYMOS-XR - Revision history

Admin at 12:53, 22 April 2026

2026-04-22T12:53:20Z

← Older revision		Revision as of 12:53, 22 April 2026
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	=== 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.
			\|}

Admin at 08:58, 22 April 2026

2026-04-22T08:58:32Z

← Older revision		Revision as of 08:58, 22 April 2026
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	{\| class='wikitable' style='margin:auto'		{\| class='wikitable' style='margin:auto'
	\|-		\|-
	! CORDIS Reference !! Start date !! End date !! Coordinator		! 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://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.

Admin: 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..."

2025-03-04T13:54:05Z

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..."

New page

=== 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 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.