WILD SAM combines denoised pseudo-labels from real adverse-weather images with simulation-based training to improve object detection AP by up to 13% on the Four Seasons dataset for rain and snow.
Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection
2 Pith papers cite this work, alongside 29 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Recent LiDAR 3D detectors remain as vulnerable to adversarial attacks as predecessors, with voxel-based and non-anchor-based models showing greater susceptibility under a multi-factor robustness framework.
citing papers explorer
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WILD SAM: A Simulated-and-Real Data Augmentation for Autonomous Driving Perception under Challenging Weather
WILD SAM combines denoised pseudo-labels from real adverse-weather images with simulation-based training to improve object detection AP by up to 13% on the Four Seasons dataset for rain and snow.
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Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving
Recent LiDAR 3D detectors remain as vulnerable to adversarial attacks as predecessors, with voxel-based and non-anchor-based models showing greater susceptibility under a multi-factor robustness framework.