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.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.
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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.
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C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation
M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.