The paper introduces adverse-weather 3D LiDAR tracking benchmarks, shows existing trackers degrade sharply, and proposes DRCT, a contrastive-learning method that improves one baseline on the synthetic benchmark.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space,
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Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions
The paper introduces adverse-weather 3D LiDAR tracking benchmarks, shows existing trackers degrade sharply, and proposes DRCT, a contrastive-learning method that improves one baseline on the synthetic benchmark.