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UID:0-404@lisn.upsaclay.fr
DTSTART;TZID=Europe/Paris:20260624T140000
DTEND;TZID=Europe/Paris:20260624T140000
DTSTAMP:20260608T200014Z
URL:https://www.lisn.upsaclay.fr/evenements/approaches-for-high-fidelity-n
 ovel-view-synthesis-from-casual-captures-to-novel-views/
SUMMARY:Approaches for High-Fidelity Novel View Synthesis: From Casual Capt
 ures to Novel Views
DESCRIPTION:This thesis investigates how the selection\, quality\, and geom
 etric arrangement of views influence the fidelity of novel view synthesis.
  The first part focuses on reconstructive approaches based on Neural Radia
 nce Fields and 3D Gaussian Splatting\, introducing methods for image deblu
 rring\, pose independent frame selection\, and view utility estimation. Th
 e second part studies generative approaches based on multiview diffusion m
 odels\, with a particular focus on camera geometry\, conditioning view arr
 angement\, sparse view novel view synthesis\, and 360° panorama generatio
 n. The results demonstrate that both input quality and camera geometry pla
 y a central role in reconstruction and generation fidelity\, while also sh
 owing that video trained multiview diffusion models can be transferred zer
 o shot to new view synthesis tasks without task specific fine tuning.\nExa
 mination Committee\nProf. Vincent Lepetit\, École des Ponts ParisTech (Re
 viewer)\nProf. Mounir Kaaniche\, Université Sorbonne Paris Nord (Reviewer
 )\nDr. Frédéric Dufaux\, CNRS Research Director\, CentraleSupélec\, Uni
 versité Paris-Saclay (Examiner)\nProf. Sylvie Le Hégarat\, Université P
 aris-Saclay (Examiner)
CATEGORIES:Thèses et HDR
LOCATION:LISN Site Plaine\, 1 rue Raimond Castaing   91190 Gif-sur-Yvette\,
  France
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DTSTART:20260329T030000
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