activity
20182026
most citedRain rendering for evaluating and improving robustness to bad weather

119 citations · 156 across the 24 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CV2019

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

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette +2

Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In th…

cs.CV2019★ 1 cited

Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation

Fabio Pizzati, Raoul de Charette, Michela Zaccaria +1

Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits…

cs.CV2019

Physics-Based Rendering for Improving Robustness to Rain

Shirsendu Sukanta Halder, Jean-François Lalonde, Raoul de Charette

To improve the robustness to rain, we present a physically-based rain rendering pipeline for realistically inserting rain into clear weather images. Our rendering relies on a physi…

cs.CV2019★ 4 cited

3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction

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. Because the object suffers from heavy hand int…

cs.CV2019

3D Surface Reconstruction from Voxel-based Lidar Data

Luis Roldão, Raoul de Charette, Anne Verroust-Blondet

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding. In this paper, a 3D surface reconstruction algorithm from heterogene…