Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Recherche

Afficher tous les résultats pour

Accueil

Personne

Michal Jan Tyszkiewicz

Cette personne a quitté l’EPFL

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (5)

Page 1 sur 1

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

A Multifaceted Spatial Analysis of Tomb Distribution in Blemmyan Berenike (eastern Desert of Egypt)

Michal Jan Tyszkiewicz

This study examines the spatial and visual organisation of tombs in the post-Roman Berenike located in Egypt's Eastern Desert. Archaeological surveys, remote sensing, geophysical methods, excavations and GIS-based analyses are used for a comprehensive unde ...

WILEY2025

GECCO: Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision community. Here, we tackle the related problem of generating point clouds, both uncondition ...

Ieee Computer Soc2023

Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...

2023

DISK: learning local features with policy gradient

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveragin ...

2020