3 papers
cs.LG2026
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Mathieu Cyrille Simon, Pascal Frossard, Christophe De Vleeschouwer
This paper explores unsupervised disentangled representation learning from a functional perspective. We define latent concepts as factors that influence observations through locall…
cs.CV2026
CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer
Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits applicability to targets with rel…
cs.CV2026
NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty
Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer
Autonomous rendezvous and proximity operations around uncooperative, unknown spacecraft are critical for active debris removal and on-orbit servicing missions. A key component of s…