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Found 6 papers, 2 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.
Panoptic Out-of-Distribution Segmentation
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task.
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.
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images
Implicit supervision trains the model by enforcing spatial consistency of the scene over time based on FV semantic sequences, while explicit supervision exploits BEV pseudolabels generated from FV semantic annotations and self-supervised depth estimates.
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research.
