17 citations · 31 across the 16 of their papers we have counts for
19 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…
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…
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…
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…
3D Semantic Scene Completion: a Survey
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. In the last years following the multiplicati…
Description and Technical specification of Cybernetic Transportation Systems: an urban transportation concept
Luis Roldão, Joshue Pérez, David González +1
The Cybernetic Transportation Systems (CTS) is an urban mobility concept based on two ideas: the car sharing and the automation of dedicated systems with door-to-door capabilities.…
LMSCNet: Lightweight Multiscale 3D Semantic Completion
Luis Roldão, Raoul de Charette, Anne Verroust-Blondet
We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehens…
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…
A Statistical Update of Grid Representations from Range Sensors
Luis Roldão, Raoul De Charette, Anne Verroust-Blondet
In a wide range of robotic applications, being able to create a 3D model of the surrounding environment is a key feature for autonomous tasks. In this research report, we present a…