119 citations · 156 across the 24 of their papers we have counts for
5 papers · 1 filter
MonoScene: Monocular 3D Semantic Scene Completion
Anh-Quan Cao, Raoul de Charette
MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from…
ManiFest: Manifold Deformation for Few-shot Image Translation
Fabio Pizzati, Jean-François Lalonde, Raoul de Charette
Most image-to-image translation methods require a large number of training images, which restricts their applicability. We instead propose ManiFest: a framework for few-shot image…
Physics-informed Guided Disentanglement in Generative Networks
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altoge…
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving
Pranav Agarwal, Pierre de Beaucorps, Raoul de Charette
Deep reinforcement Learning for end-to-end driving is limited by the need of complex reward engineering. Sparse rewards can circumvent this challenge but suffers from long training…
CoMoGAN: continuous model-guided image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normaliza…