5 papers · 1 filter
FreeOcc: Training-free Panoptic Occupancy Prediction via Foundation Models
Andrew Caunes, Thierry Chateau, Vincent Fremont
Semantic and panoptic occupancy prediction for road scene analysis provides a dense 3D representation of the ego vehicle's surroundings. Current camera-only approaches typically re…
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation
Andrew Caunes, Thierry Chateau, Vincent Frémont
Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive ann…
Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation
Andrew Caunes, Thierry Chateau, Vincent Fremont
3D semantic segmentation plays a pivotal role in autonomous driving and road infrastructure analysis, yet state-of-the-art 3D models are prone to severe domain shift when deployed…
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow
Minh-Quan Dao, Vincent Frémont, Elwan Héry
Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a po…