119 citations · 122 across the 4 of their papers we have counts for
14 papers
Material Palette: Extraction of Materials from a Single Image
Ivan Lopes, Fabio Pizzati, Raoul de Charette
In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image t…
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation
Rong Li, Anh-Quan Cao, Raoul de Charette
Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several o…
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…
Rain rendering for evaluating and improving robustness to bad weather
Maxime Tremblay, Shirsendu Sukanta Halder, Raoul de Charette +1
Rain fills the atmosphere with water particles, which breaks the common assumption that light travels unaltered from the scene to the camera. While it is well-known that rain affec…
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
Etienne Dubeau, Mathieu Garon, Benoit Debaque +2
Augmented reality devices require multiple sensors to perform various tasks such as localization and tracking. Currently, popular cameras are mostly frame-based (e.g. RGB and Depth…
Model-based occlusion disentanglement for image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised mode…