Despoina Paschalidou
Despoina Paschalidou
PhD Candidate in Max Planck Institute for Intelligent Systems and ETH Zürich
Verified email at tuebingen.mpg.de - Homepage
Title
Cited by
Cited by
Year
Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
D Paschalidou, AO Ulusoy, A Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019, 2019
682019
Pointflownet: Learning representations for rigid motion estimation from point clouds
A Behl, D Paschalidou, S Donné, A Geiger
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
672019
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
D Paschalidou, AO Ulusoy, C Schmitt, L van Gool, A Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018, 2018
462018
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
D Paschalidou, L van Gool, A Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020, 2020
252020
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
D Paschalidou, A Katharopoulos, A Geiger, S Fidler
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021, 2021
62021
Fast supervised LDA for discovering micro-events in large-scale video datasets
A Katharopoulos, D Paschalidou, C Diou, A Delopoulos
Proceedings of the 24th ACM international conference on Multimedia, 332-336, 2016
52016
Learning local feature aggregation functions with backpropagation
A Katharopoulos, D Paschalidou, C Diou, A Delopoulos
2017 25th European Signal Processing Conference (EUSIPCO), 748-752, 2017
12017
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
D Paschalidou, A Kar, M Shugrina, K Kreis, A Geiger, S Fidler
Thirty-Fifth Conference on Neural Information Processing Systems, 2021
2021
Supplementary Material for Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
D Paschalidou, AO Ulusoy, A Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019, 2019
2019
Supplementary Material for Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks.
D Paschalidou, A Katharopoulos, A Geiger, S Fidler
Supplementary Material for Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
D Paschalidou, L van Gool, A Geiger
Supplementary Material for RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
D Paschalidou, AO Ulusoy, C Schmitt, L van Gool, A Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018, 0
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