1 citations · 1 across the 8 of their papers we have counts for
10 papers
SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch +3
This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring complex architectural redesigns. T…
Pictura: Perspective-View Self-Play at Scale for Driving
Yuan Yin, Elias Ramzi, Marc Lafon +8
Self-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observations such as exact poses and v…
EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models
Fatima Balde, Raoul de Charette, Alexandre Boulch
3D semantic scene generation is crucial for autonomous driving applications, yet most methods rely on complex 3D-specific architectures such as triplane encoders and adapted diffus…
Test-Time Trajectory Optimization for Autonomous Driving
Yihong Xu, Eloi Zablocki, Yuan Yin +4
End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate. However, the scorer is appl…
Exploring Easy Boosts for Lidar Semantic Scene Completion
Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch +3
This paper investigates "free lunch" strategies to boost the performance of lidar semantic scene completion (SSC) without requiring complex architectural redesigns. We first demons…
VaViT: Vanilla ViT for Automotive Point Cloud Semantic Segmentation
Gilles Puy, Nermin Samet, Alexandre Boulch +3
Plain Transformers have become the de-facto architecture for processing text, audio, image, and video, offering a unified backbone for multimodal learning. However, state-of-the-ar…
Driving on Registers
Ellington Kirby, Alexandre Boulch, Yihong Xu +11
We present DrivoR, a simple and efficient transformer-based architecture for end-to-end autonomous driving. Our approach builds on pretrained Vision Transformers (ViTs) and introdu…
NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering
Loick Chambon, Paul Couairon, Eloi Zablocki +3
Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off:…
Improving Multimodal Distillation for 3D Semantic Segmentation under Domain Shift
Björn Michele, Alexandre Boulch, Gilles Puy +3
Semantic segmentation networks trained under full supervision for one type of lidar fail to generalize to unseen lidars without intervention. To reduce the performance gap under do…
SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering
Seamie Hayes, Alexandre Boulch, Andrei Bursuc +4
Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datase…