Search Results for author: Raoul de Charette

Found 29 papers, 17 papers with code

UMBRAE: Unified Multimodal Brain Decoding

1 code implementation10 Apr 2024 Weihao Xia, Raoul de Charette, Cengiz Öztireli, Jing-Hao Xue

We address prevailing challenges of the brain-powered research, departing from the observation that the literature hardly recover accurate spatial information and require subject-specific models.

Brain Decoding Language Modelling +2

PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness

1 code implementation CVPR 2024 Anh-Quan Cao, Angela Dai, Raoul de Charette

We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene.

Autonomous Driving

A Simple Recipe for Language-guided Domain Generalized Segmentation

1 code implementation CVPR 2024 Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette

Generalization to new domains not seen during training is one of the long-standing challenges in deploying neural networks in real-world applications.

Data Augmentation Semantic Segmentation

Material Palette: Extraction of Materials from a Single Image

no code implementations CVPR 2024 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.

Unsupervised Domain Adaptation

DREAM: Visual Decoding from Reversing Human Visual System

1 code implementation3 Oct 2023 Weihao Xia, Raoul de Charette, Cengiz Öztireli, Jing-Hao Xue

In this work we present DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system.

PODA: Prompt-driven Zero-shot Domain Adaptation

1 code implementation ICCV 2023 Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette

In this paper, we propose the task of 'Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general description in natural language of the target domain, i. e., a prompt.

Image Classification Language Modelling +7

PØDA: Prompt-driven Zero-shot Domain Adaptation

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

In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general description in natural language of the target domain, i. e., a prompt.

Image Classification object-detection +5

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

2 code implementations ICCV 2023 Anh-Quan Cao, Raoul de Charette

3D reconstruction from a single 2D image was extensively covered in the literature but relies on depth supervision at training time, which limits its applicability.

3D Reconstruction 3D Scene Reconstruction +5

Cross-task Attention Mechanism for Dense Multi-task Learning

2 code implementations17 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

1 code implementation29 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

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 Position +1

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 +4

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 +2

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 +2

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 object-detection +3

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 +1

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 +3

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