Search Results for author: Raoul de Charette

Found 23 papers, 10 papers with code

PØDA: Prompt-driven Zero-shot Domain Adaptation

1 code implementation6 Dec 2022 Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette

Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some conditions, especially for long-tail samples.

SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

no code implementations5 Dec 2022 Anh-Quan Cao, Raoul de Charette

As the latter are conditioned on a single frame, scene reconstruction is achieved from the fusion of multiple synthesized novel depth views.

Cross-task Attention Mechanism for Dense Multi-task Learning

1 code implementation17 Jun 2022 Ivan Lopes, Tuan-Hung Vu, Raoul de Charette

Multi-task learning has recently become a promising solution for a comprehensive understanding of complex scenes.

2D Semantic Segmentation Multi-Task Learning +4

MonoScene: Monocular 3D Semantic Scene Completion

1 code implementation CVPR 2022 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.

3D Semantic Scene Completion from a single RGB image

ManiFest: Manifold Deformation for Few-shot Image Translation

1 code implementation26 Nov 2021 Fabio Pizzati, Jean-François Lalonde, Raoul de Charette

To enforce feature consistency, our framework learns a style manifold between source and proxy anchor domains (assumed to be composed of large numbers of images).

Image-to-Image Translation Translation

Leveraging Local Domains for Image-to-Image Translation

no code implementations9 Sep 2021 Anthony Dell'Eva, Fabio Pizzati, Massimo Bertozzi, Raoul de Charette

Our comprehensive evaluation setting shows we are able to generate realistic translations, with minimal priors, and training only on a few images.

Image-to-Image Translation Transfer Learning +1

Physics-informed Guided Disentanglement in Generative Networks

no code implementations29 Jul 2021 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 altogether the translation quality, controllability and variability.

Disentanglement Image-to-Image Translation +1

Goal-constrained Sparse Reinforcement Learning for End-to-End Driving

no code implementations16 Mar 2021 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.

reinforcement-learning

3D Semantic Scene Completion: a Survey

no code implementations12 Mar 2021 Luis Roldao, Raoul de Charette, Anne Verroust-Blondet

Semantic Scene Completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input.

3D Semantic Scene Completion 3D Semantic Segmentation

CoMoGAN: continuous model-guided image-to-image translation

2 code implementations CVPR 2021 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.

Image-to-Image Translation Translation

Rain rendering for evaluating and improving robustness to bad weather

no code implementations6 Sep 2020 Maxime Tremblay, Shirsendu Sukanta Halder, Raoul de Charette, Jean-François Lalonde

In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of rain.

Depth Estimation object-detection +2

RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking

1 code implementation9 Jun 2020 Etienne Dubeau, Mathieu Garon, Benoit Debaque, Raoul de Charette, Jean-François Lalonde

In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.

3D Object Tracking Camera Calibration +1

Model-based occlusion disentanglement for image-to-image translation

no code implementations ECCV 2020 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.

Disentanglement Image-to-Image Translation +1

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

1 code implementation CVPR 2020 Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, Patrick Pérez

In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation.

3D Semantic Segmentation Autonomous Driving +1

Physics-Based Rendering for Improving Robustness to Rain

no code implementations ICCV 2019 Shirsendu Sukanta Halder, Jean-François Lalonde, Raoul de Charette

Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog.

object-detection Object Detection +1

3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction

no code implementations5 Aug 2019 Raoul de Charette, Sotiris Manitsaris

We reconstruct 3D deformable object through time, in the context of a live pottery making process where the crafter molds the object.

3D Reconstruction Object Reconstruction

3D Surface Reconstruction from Voxel-based Lidar Data

no code implementations25 Jun 2019 Luis Roldão, Raoul de Charette, Anne Verroust-Blondet

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding.

Autonomous Navigation Surface Reconstruction

End-to-End Race Driving with Deep Reinforcement Learning

no code implementations6 Jul 2018 Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Perot, Fawzi Nashashibi

We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding).

Domain Adaptation Object Recognition +2

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