Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.
Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.
At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.