17 citations · 41 across the 19 of their papers we have counts for
20 papers
HELIOS: From midnight to noon, continuous outdoor urban scene relighting
Hala Djeghim, Nathan Piasco, Luis Roldão +4
Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely on synthetic data or paired m…
Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
Thang-Anh-Quan Nguyen, Moussab Bennehar, Luis Guillermo Roldao Jimenez +5
Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adver…
DrivingVoxels: Compositional Sparse Voxel Rasterization for Dynamic Driving Scene Reconstruction
Tania Aguirre, Luis Roldão, Moussab Bennehar +4
Reconstructing dynamic urban scenes remains challenging due to the unbounded nature of driving environments and the presence of multiple dynamic objects. Currently, potentially fas…
SEM-ROVER: Semantic Voxel-Guided Diffusion for Large-Scale Driving Scene Generation
Hiba Dahmani, Nathan Piasco, Moussab Bennehar +5
Scalable generation of outdoor driving scenes requires 3D representations that remain consistent across multiple viewpoints and scale to large areas. Existing solutions either rely…
LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding
Fusang Wang, Nathan Piasco, Moussab Bennehar +3
Recent advancements in open-vocabulary 3D scene understanding heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into 3D space. However, we identify…
SAIL: Self-supervised Albedo Estimation from Real Images with a Latent Diffusion Model
Hala Djeghim, Nathan Piasco, Luis Roldão +4
Intrinsic image decomposition aims at separating an image into its underlying albedo and shading components, isolating the base color from lighting effects to enable downstream app…