Albert Matveev
Professor: Evgeny Burnaev
Graduated from: National Research University Higher School of Economics
Research interests: geometric deep learning, geometry processing
Thesis topic: Learnable 3D Geometry Reconstruction with Applications in Engineering
Publications:
– Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., & Panozzo, D. (2019). ABC: A Big CAD Model Dataset For Geometric Deep Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9601-9611).
– Taktasheva*, M., Matveev*, A., Artemov, A., Burnaev, E. (2019). Learning to Approximate Directional Fields Defined over 2D Planes. In Proceedings of the International Conference on Analysis of Images, Social Networks and Texts.
Conferences:
– Poster: Learnable Geometric Features for 3D Geometry Reconstruction. Skoltech-MIT conference “Collaborative Solutions for Next Generation Education, Science and Technology”. Oct. 16 2018.
– Talk: Learnable Geometric Features for 3D Geometry Reconstruction. The 1st International Workshop on Tensor Networks and Machine Learning. Oct. 25 2018.
– Poster: ABC: A Big CAD Model Dataset For Geometric Deep Learning. IEEE Conference on Computer Vision and Pattern Recognition. Jun. 20 2019.
– Talk: Learning to Approximate Directional Fields Defined over 2D Planes. International Conference on Analysis of Images, Social Networks and Texts. Jul. 17 2019.
