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Machine Perception Research Laboratory

The Machine Perception Research Laboratory is dedicated to the intelligent interpretation of the environment. Our main research areas include autonomous vehicle sensing, analysis of 3D models and LIDAR data, and recognition of biometric features based on motion and gestures. We are also involved in object search and tracking and change detection using data from different sensors. Special attention is paid to data fusion, scaling up and compression. We use remote sensing data to support biodiversity and environmental modelling research. Our laboratory also researches and develops machine learning techniques for practical applications. We perform mapping and monitoring tasks in collaboration with ground and airborne robots, as well as medical image processing. In addition to our work, we attach great importance to the modernisation of science and participation in university education.

Head of department

Tamás Szirányi

+36 1 279 6106
@email
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Secretary

Mónika Barti

+36 1 279 6132
@email
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Project manager

Katalin Bános, Ph.D.

+36 1 279 6178
@email
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Project assistant

Anikó Vágvölgyi

+36 1 873 6532
@email
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Members

View them in our Directory

Contact info

Address
1111 Budapest, Kende u. 13-17.
Phone
+36 1 279 6106
Fax
+36 1 279 6292

Research Areas and Professional Activities

  • Methodological Research
    • Environmental perception for autonomous vehicles and mobile robots
    • Fusion of different sensing modalities
    • Upscaling and compressing LIDAR data
    • Research, development, and practical application of machine learning methods
  • Applications
    • Object detection and tracking using camera, radar, and LIDAR data
    • Change detection in terrestrial, aerial, and remote sensing data
    • Mapping and monitoring using combined terrestrial and aerial robotics
    • Supporting biodiversity and environmental modeling research through remote sensing data
    • Recognition and tracking of biometric features (e.g., motion and gestures)
    • Medical image processing
  • Dissemination and Education
    • Science communication (popular science lectures, Researchers’ Night, AI Summit, AI Symposium)
    • Education and talent development (BME, PPKE)
    • Supervising PhD, thesis, and professional internship projects

Research Projects

Innovation Projects

Selected Publications

  • Kovács, L., & Bódis, B. M., & Benedek, C. (2024). LidPose: Real-Time 3D human pose estimation in sparse Lidar point clouds with non-repetitive circ scanning pattern. Sensors, 24(11), Article 3427, 25 pages. https://doi.org/10.3390/s24113427
  • Keszler, A., & Tuza, Z. (2024). Spectrum of 3-uniform 6- and 9-cycle systems over K(3)v − I. Discrete Mathematics, 347(3), Article 113782, 10 pages. https://doi.org/10.1016/j.disc.2023.113782
  • Rózsa, Z., & Szirányi, T. (2023). Optical flow and expansion based deep temporal up-sampling of LIDAR point clouds. Remote Sensing, 15(10), Article 2487, 19 pages. https://doi.org/10.3390/rs15102487
  • Ibrahim, Y., & Benedek, C. (2023). MVPCC-Net: Multi-view based point cloud completion network for MLS data. Image and Vision Computing, 134, Article 104675, 13 pages. https://doi.org/10.1016/j.imavis.2023.104675
  • Zováthi, Ö., & Pálffy, B., & Jankó, Z., & Benedek, C. (2023). ST-DepthNet: A spatio-temporal deep network for depth completion using a single non-repetitive circular scanning Lidar. IEEE Robotics and Automation Letters, 8(6), 3270–3277, 8 pages. https://doi.org/10.1109/LRA.2023.3266670
  • Rózsa, Z., & Golarits, M., & Szirányi, T. (2022). Immediate vehicle movement estimation and 3D reconstruction for mono cameras by utilizing epipolar geometry and direction prior. IEEE Transactions on Intelligent Transportation Systems, 23(12), 23548–23558, 11 pages. https://doi.org/10.1109/TITS.2022.3199046
  • Barath, D., & Matas, J. (2022). Graph-Cut RANSAC: Local optimization on spatially coherent structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 4961–4974, 14 pages. https://doi.org/10.1109/TPAMI.2021.3071812
  • Barath, D., & Noskova, J., & Matas, J. (2022). Marginalizing sample consensus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 8420–8432, 13 pages. https://doi.org/10.1109/TPAMI.2021.3103562
  • Göncz, L., & Majdik, A. (2022). Object-based change detection algorithm with a spatial AI stereo camera. Sensors, 22(17), Article 6342, 16 pages. https://doi.org/10.3390/s22176342
  • Liu, C., & Szirányi, T. (2022). Road condition detection and emergency rescue recognition using on-board UAV in the wilderness. Remote Sensing, 14(17), Article 4355, 27 pages. https://doi.org/10.3390/rs14174355
  • Zováthi, Ö., & Nagy, B., & Benedek, C. (2022). Point cloud registration and change detection in urban environment using an onboard Lidar sensor and MLS reference data. International Journal of Applied Earth Observation and Geoinformation, 110, Article 102767, 13 pages. https://doi.org/10.1016/j.jag.2022.102767

Patents

Awarded

Submitted