5 citations · 8 across the 7 of their papers we have counts for
8 papers
StyleFields: Multi-Scale AdaIN-Modulated Implicit SDFs for Coarse-to-Fine 3D Shape Reconstruction and Editing
Ehsan Garaaghaji, Nicolas Talabot, Pascal Fua +1
We introduce StyleFields, a DeepSDF-based architecture for high-fidelity 3D reconstruction that enables controllable geometric style mixing: the coarse structure of one object can…
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Nicolas Talabot, Olivier Clerc, Arda Cinar Demirtas +4
Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-…
IT: Idempotent Test-Time Training
Nikita Durasov, Assaf Shocher, Doruk Oner +3
Deep learning models often struggle when deployed in real-world settings due to distribution shifts between training and test data. While existing approaches like domain adaptation…
Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft
Jakub Gwizdała, Doruk Oner, Soumava Kumar Roy +6
Power lines are dangerous for low-flying aircraft, especially in low-visibility conditions. Thus, a vision-based system able to analyze the aircraft's surroundings and to provide t…
Enabling Uncertainty Estimation in Iterative Neural Networks
Nikita Durasov, Doruk Oner, Jonathan Donier +2
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that…
Enforcing connectivity of 3D linear structures using their 2D projections
Doruk Oner, Hussein Osman, Mateusz Kozinski +1
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural ne…
Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate
Doruk Oner, Leonardo Citraro, Mateusz Koziński +1
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet, in practice, people, no matter how conscientious, have trouble…
Persistent Homology with Improved Locality Information for more Effective Delineation
Doruk Oner, Adélie Garin, Mateusz Koziński +2
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing me…