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Autonomous mobile robots rely on object detection for navigation and safety, yet edge-deployed detectors remain vulnerable to adversarial patches under limited compute, power, and latency constraints. This paper presents a deploymentoriented robustness benchmark using nine CARLA-GeAR billboard scenarios and five detect...
Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerous mitigation techniques, their effectiveness in 3D remains unclear. We benchmark six reweighting schemes and five imbalance-aware losses across three datasets (DALES, S...
This work provides the first mechanistic explanation for why techniques proven effective in 2D do not readily transfer to point-based 3D point cloud segmentation, validated across two representative architectures.
Experiments show that ChACo improves over prior local-learning baselines, transfers across WAN and ResNet convolutional architectures, and narrows the gap to matched backpropagation (BP) references, while end-to-end BP remains stronger in the most depth-dependent settings.
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