Yao Wei is promovendus bij de afdeling Aardobservatiewetenschap (EOS), Faculteit ITC, Universiteit Twente. Haar onderzoeksinteresses omvatten computervisie en diep leren met specialisatie op het begrijpen van 3D-scènes.
Daarvoor werkte ze op het gebied van de extractie van geo-informatie uit satellietbeelden. Zij ontving de M.S. graad in fotogrammetrie en remote sensing van Wuhan University.
Expertises
Computer Science
- Generative Adversarial Networks
- Point Cloud
- Remote Sensing Image
- Surface Extraction
- Deep Learning
- Convolutional Neural Network
- Segmentation
Social Sciences
- Remote Sensing
Organisaties
Publicaties
2023
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models (2023)In IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 2910-2919). IEEE. Wei, Y., Vosselman, G. & Yang, M. Y.
2022
Flow-based GAN for 3D Point Cloud Generation from a Single Image (2022)In 33rd British Machine Vision Conference 2022: London, UK, November 21-24, 2022. BMVA Press. Wei, Y., Vosselman, G. & Yang, M.https://bmvc2022.mpi-inf.mpg.de/569/Flow-based GAN for 3D point cloud generation from a single image (2022)[Working paper › Preprint]. ArXiv.org. Wei, Y., Vosselman, G. & Yang, M. Y.https://doi.org/10.48550/arXiv.2210.04072A combination of convolutional and graph neural networks for regularized road surface extraction (2022)IEEE transactions on geoscience and remote sensing, 60. Article 4409113. Yan, J., Ji, S. & Wei, Y.https://doi.org/10.1109/TGRS.2022.3151688Scribble-based weakly supervised deep learning for road surface extraction from remote sensing images (2022)IEEE transactions on geoscience and remote sensing, 60, 1-12. Article 9372390. Wei, Y. & Ji, S.https://doi.org/10.1109/TGRS.2021.3061213
2020
Simultaneous road surface and centerline extraction from large-scale remote sensing images using CNN-based segmentation and tracing (2020)IEEE transactions on geoscience and remote sensing, 58(12), 8919-8931. Article 9094008. Wei, Y., Zhang, K. & Ji, S.https://doi.org/10.1109/TGRS.2020.2991733
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