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Found 5 papers, 2 papers with code
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties.
Uncertainty-aware LiDAR Panoptic Segmentation
Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties.
Uncertainty-aware Panoptic Segmentation
In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty 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.
EfficientLPS: Efficient LiDAR Panoptic Segmentation
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors.
