Project Details
Topografická analýza obrazu s využitím metod hlubokého učení
Project Period: 1. 7. 2019 – 30. 6. 2022
Project Type: grant
Code: LTAIZ19004
Agency: Ministerstvo školství, mládeže a tělovýchovy ČR
Program: INTER-EXCELLENCE - Podprogram INTER-ACTION
image geo-localization, topographic information, image registration,
deep-learning, computer vision
The project focuses on the current problems of computer vision, especially on
visual localization in the natural environment. The visual location of the camera
in the outdoor environment is not a fixed issue today, although it offers a wide
range of attractive applications from automatic image comprehension, to expanded
reality applications to navigation of self-governing vehicles and airplanes. The
project aims to research new methods for locating cameras based on the multimodal
data registration, especially photographic information, synthetic rendered
images, depth information and field models using current machine learning
methods, especially deep neural networks (DNN). In addition to the use of terrain
data in the form of graphical models, an alternative of predictive depth
information from an input photograph will be explored. The CPhoto @ FIT Group has
been dealing with the long-standing problem and has deep experience in research
and application. The Israeli partner also offers unique data sets indispensable
for DNN training.
Brejcha Jan, Ing., Ph.D. (RG CPHOTO)
Lysek Tomáš, Ing.
Polášek Tomáš, Ing. (DCGM)
Tomešek Jan, Ing. (DCGM)
2024
- BOBÁK, P.; ČMOLÍK, L.; ČADÍK, M. Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2024, vol. 30, no. 9,
p. 5908-5922. ISSN: 1077-2626. Detail - DEKEL, S.; KELLER, Y.; ČADÍK, M. Estimating Extreme 3D Image Rotations using Cascaded Attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE Computer Society, 2024.
p. 2588-2598. ISBN: 979-8-3503-5301-3. Detail
2023
- POLÁŠEK, T.; ČADÍK, M.; KELLER, Y.; BENEŠ, B. Vision UFormer: Long-Range Monocular Absolute Depth Estimation. COMPUTERS & GRAPHICS-UK, 2023, vol. 111, no. 4,
p. 180-189. ISSN: 0097-8493. Detail
2022
- RAJASEKARAN, S.; KANG, H.; ČADÍK, M.; GALIN, E.; GUÉRIN, E.; PEYTAVIE, A.; SLAVÍK, P.; BENEŠ, B. PTRM: Perceived Terrain Realism Metric. ACM Transactions on Applied Perception, 2022, vol. 19, no. 2,
p. 1-22. ISSN: 1544-3558. Detail - TOMEŠEK, J.; ČADÍK, M.; BREJCHA, J. CrossLocate: Cross-Modal Large-Scale Visual Geo-Localization in Natural Environments using Rendered Modalities. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: Institute of Electrical and Electronics Engineers, 2022.
p. 2193-2202. ISBN: 978-1-6654-0477-8. Detail
2021
- AHMAD, T.; EMAMI, E.; ČADÍK, M.; BEBIS, G. Resource Efficient Mountainous Skyline Extraction using Shallow Learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN). Hoffman Estates: Institute of Electrical and Electronics Engineers, 2021.
p. 1-9. ISBN: 978-1-6654-3900-8. Detail - POLÁŠEK, T.; HRŮŠA, D.; BENEŠ, B.; ČADÍK, M. ICTree: Automatic Perceptual Metrics for Tree Models. ACM TRANSACTIONS ON GRAPHICS, 2021, vol. 40, no. 6,
p. 1-15. ISSN: 0730-0301. Detail
2020
- BOBÁK, P.; ČMOLÍK, L.; ČADÍK, M. Temporally Stable Boundary Labeling for Interactive and Non-Interactive Dynamic Scenes. COMPUTERS & GRAPHICS-UK, 2020, vol. 91, no. 10,
p. 265-278. ISSN: 0097-8493. Detail - BREJCHA, J.; LUKÁČ, M.; HOLD-GEOFFROY, Y.; WANG, O.; ČADÍK, M. LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors. In Computer Vision - ECCV 2020. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland AG, 2020.
p. 295-312. ISBN: 978-3-030-58525-9. Detail
2023
- Large-scale outdoor augmented reality scenes using camera pose based on learned descriptors, patent, 2023
Authors: LUKÁČ, M.; WANG, O.; BREJCHA, J.; HOLD-GEOFFROY, Y.; ČADÍK, M.