59 citations · 112 across the 26 of their papers we have counts for
28 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…
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Kai Chen, Ruiyuan Gao, Lanqing Hong +11
In this paper, we present details of the 1st W-CODA workshop, held in conjunction with the ECCV 2024. W-CODA aims to explore next-generation solutions for autonomous driving corner…
PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenes
Christina Ourania Tze, Daniel Dauner, Yiyi Liao +2
Existing approaches to 3D semantic urban scene generation predominantly rely on voxel-based representations, which are bound by fixed resolution, challenging to edit, and memory-in…
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…
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
Quentin Herau, Nathan Piasco, Moussab Bennehar +6
Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and e…
J-NeuS: Joint field optimization for Neural Surface reconstruction in urban scenes with limited image overlap
Fusang Wang, Hala Djeghim, Nathan Piasco +6
Reconstructing the surrounding surface geometry from recorded driving sequences poses a significant challenge due to the limited image overlap and complex topology of urban environ…
Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis
Thang-Anh-Quan Nguyen, Nathan Piasco, Luis Roldão +5
Synthesizing extrapolated views remains a difficult task, especially in urban driving scenes, where the only reliable sources of data are limited RGB captures and sparse LiDAR poin…
SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians
Hiba Dahmani, Moussab Bennehar, Nathan Piasco +2
Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large c…
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
Quentin Herau, Moussab Bennehar, Arthur Moreau +6
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have…
ViiNeuS: Volumetric Initialization for Implicit Neural Surface reconstruction of urban scenes with limited image overlap
Hala Djeghim, Nathan Piasco, Moussab Bennehar +3
Neural implicit surface representation methods have recently shown impressive 3D reconstruction results. However, existing solutions struggle to reconstruct driving scenes due to t…
RoDUS: Robust Decomposition of Static and Dynamic Elements in Urban Scenes
Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco +2
The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years. However, capturing large-scale scenes still poses a challenge d…
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To corr…
PlaNeRF: SVD Unsupervised 3D Plane Regularization for NeRF Large-Scale Scene Reconstruction
Fusang Wang, Arnaud Louys, Nathan Piasco +3
Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images and camera poses for Novel View Synthesis (NVS). Although NeRF can produce photorealistic results, it of…
CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation
Arthur Moreau, Nathan Piasco, Moussab Bennehar +3
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene an…
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the informat…
Exploiting map information for self-supervised learning in motion forecasting
Caio Azevedo, Thomas Gilles, Stefano Sabatini +1
Inspired by recent developments regarding the application of self-supervised learning (SSL), we devise an auxiliary task for trajectory prediction that takes advantage of map-only…
Exploring the trade off between human driving imitation and safety for traffic simulation
Yann Koeberle, Stefano Sabatini, Dzmitry Tsishkou +1
Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is requir…
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so…
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
Arthur Moreau, Thomas Gilles, Nathan Piasco +3
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine…
LENS: Localization enhanced by NeRF synthesis
Arthur Moreau, Nathan Piasco, Dzmitry Tsishkou +2
Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis. In this paper, we propose to apply novel view synthesis to th…