Despoina Paschalidou

I am a researcher in Computer Vision and Graphics. I am very excited about developing representations that can reliably perceive, capture and recreate the 3D world in a way that humans can interact with it as seamlessly as possible. Throughout the years, I have worked on many exciting problems ranging from 3D reconstruction of objects using interpretable primitive-based representations, generative models for objects, scenes and videos, 3D reconstruction of humans from video data, as well as on several perception tasks such as 3D point cloud reconstruction and segmentation, flow estimation, localization and collision avoidance from egocentric observations. Currently, I am a Senior Research Scientist at the NVIDIA Toronto AI Lab, based in Santa Clara.

Previously, I received my PhD from the Max Planck ETH Center for Learning Systems , where I was advised by Andreas Geiger and Luc van Gool, and I was a Postdoctoral Researcher at Stanford University with Leonidas Guibas. Prior to this, I did my undergraduate in the School of Electrical and Computer Engineering in the Aristotle University of Thessaloniki in Greece, where I worked with Anastasios Delopoulos and Christos Diou. During my PhD, I was very lucky to have spent one wonderful year working with Sanja Fidler at NVIDIA Research, and 6 months at Facebook AI Research, where I worked with David Novotny and Andrea Vedaldi.

Outside of work, I enjoy hiking, skiing, aerial yoga, learning foreign languages (currently I am struggling with Spanish) and cooking!

Selected Publications
CAD: Photorealistic 3D Generation via Adversarial Distillation
Computer Vision and Pattern Recognition (CVPR), 2024
CurveCloudNet: Processing Point Clouds with 1D Structure
Computer Vision and Pattern Recognition (CVPR), 2024
CC3D: Layout-Conditioned Generation of Compositional 3D Scenes
International Conference on Computer Vision (ICCV), 2023
PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Computer Vision and Pattern Recognition (CVPR), 2023
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
Computer Vision and Pattern Recognition (CVPR), 2023
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Advances in Neural Information Processing Systems (NeurIPS), 2021
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
Computer Vision and Pattern Recognition (CVPR), 2021
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
Computer Vision and Pattern Recognition (CVPR), 2020
Despoina Paschalidou, Luc van Gool, Andreas Geiger
Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
Computer Vision and Pattern Recognition (CVPR), 2019
Despoina Paschalidou, Ali Osman Ulusoy, Andreas Geiger