Uncertainty-Aware Panoptic Segmentation
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Benchmarks
Most implemented
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Uncertainty-aware LiDAR Panoptic Segmentation
Uncertainty-aware Panoptic Segmentation
Papers
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions. However, existing semantic per…
Autonomous VehiclesObject DetectionPanoptic SegmentationSemantic Segmentation+1ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation
We introduce ProPanDL, a family of networks capable of uncertainty-aware panoptic segmentation. Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic…
Panoptic Segmentationscoring ruleSegmentationUncertainty-Aware Panoptic SegmentationUncertainty-aware LiDAR Panoptic Segmentation
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current learning-based methods typically try to achi…
Autonomous DrivingPanoptic SegmentationScene UnderstandingSegmentation+1Uncertainty-aware Panoptic Segmentation
Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of the …
Panoptic SegmentationScene UnderstandingSegmentationUncertainty-Aware Panoptic Segmentation