This invention discloses a 3D target detection method based on sparse dynamic attention and star-shaped interaction. The method obtains basic
voxel features from the original
LiDAR point cloud through voxelization and sparse
convolution, then introduces a sparse dynamic parallel attention module. This module achieves efficient enhancement of global context and channel dimensions through dynamic attention branches and
parallel channel interaction branches. A sparse star-shaped interaction module is then used to construct a star-shaped neighborhood
interaction structure with a central
voxel, completing local
geometric modeling and nonlinear feature interaction only on non-empty voxels. Finally, keypoint sampling, RoI
pooling, and a detection head output the 3D detection box, category, and
confidence score. This invention, through the synergistic complementarity of SDPA and SSB, significantly improves the detection accuracy of long-distance, small-scale, and occluded targets while maintaining
linear growth in computational complexity and meeting real-time requirements. It achieves balanced performance optimization across multiple categories, including vehicles, pedestrians, and cyclists, and is suitable for 3D
perception scenarios with high precision and real-time requirements, such as autonomous driving.