A clustering-based unsupervised method separates a drone's lidar point cloud from noisy backgrounds and reconstructs its 3D trajectory, reported as 4th place in a CVPR 2024 challenge.
Unmanned aerial vehicle visual detection and tracking using deep neural networks: A performance benchmark,
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Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge
A clustering-based unsupervised method separates a drone's lidar point cloud from noisy backgrounds and reconstructs its 3D trajectory, reported as 4th place in a CVPR 2024 challenge.