Search Results for author:
Found 6 papers, 4 papers with code
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots.
Few-Shot Panoptic Segmentation With Foundation Models
Concurrently, recent breakthroughs in visual representation learning have sparked a paradigm shift leading to the advent of large foundation models that can be trained with completely unlabeled images.
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments.
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application.
FSOCO: The Formula Student Objects in Context Dataset
This paper presents the FSOCO dataset, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions.
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks
Domain gaps of sensor modalities pose a challenge for the design of autonomous robots.
