CV4DT


The Computer Vision for Digital Twins (CV4DT) is a research group based at the University of Cambridge and led by Olaf Wysocki. The CV4DT centres on developing methods and datasets for pushing the boundaries of 3D computer vision for accurate transfer of reality into the digital world to enable simulation before any action is taken.

  • 2026-01 — We'll be co-organsing 2x CVPR 2026 workshops: PBVS and DriveX!
  • 2025-12 — 3x 3D Computer Vision and Robotics projects together with TUM (more)
  • 2025-11 — Brian gives a keynote about multimodality at BMVC 2025!
  • 2025-10 — Our TrueCity paper goes to 3DV 2026!
Older news
  • 2025-08 — The CV4DT is born!

Latest Papers

Duc Nguyen, Yan-Ling Lai, Qilin Zhang, Prabin Gyawali, Benedikt Schwab, Olaf Wysocki, Thomas H Kolbe (2025). TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding.
3DV ‘26.

Cite arXiv PDF

Shuhao Kang, Martin Y Liao, Yan Xia, Olaf Wysocki, Boris Jutzi, Daniel Cremers (2025). OPAL: Visibility-aware Lidar-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion.
CoRL ‘25.

Cite arXiv PDF

Wenzhao Tang, Weihang Li, Xiucheng Liang, Olaf Wysocki, Filip Biljecki, Christoph Holst, Boris Jutzi (2025). Texture2LoD3: Enabling LoD3 Building Reconstruction With Panoramic Images.
CVPR ‘25.

Cite arXiv PDF Project page

Yuan Luo, Rudolf Hoffmann, Yan Xia, Olaf Wysocki, Benedikt Schwab, Thomas H Kolbe, Daniel Cremers (2025). RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning.
CVPR ‘25.

Cite arXiv PDF Project page

Olaf Wysocki, Yue Tan, Thomas Froech, Yan Xia, Magdalena Wysocki, Ludwig Hoegner, Daniel Cremers, Christoph Holst (2025). Zaha: Introducing the level of facade generalization and the large-scale point cloud facade semantic segmentation benchmark dataset.
WACV ‘25.

Cite arXiv PDF Project page

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