  In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds,
providing reliable geometric information. However, traditional sampling methods
of preprocessing often ignore semantic features, leading to detail loss and
ground point interference in 3D object detection. To address this, we propose a
multi-branch two-stage 3D object detection framework using a Semantic-aware
Multi-branch Sampling (SMS) module and multi-view consistency constraints. The
SMS module includes random sampling, Density Equalization Sampling (DES) for
enhancing distant objects, and Ground Abandonment Sampling (GAS) to focus on
non-ground points. The sampled multi-view points are processed through a
Consistent KeyPoint Selection (CKPS) module to generate consistent keypoint
masks for efficient proposal sampling. The first-stage detector uses
multi-branch parallel learning with multi-view consistency loss for feature
aggregation, while the second-stage detector fuses multi-view data through a
Multi-View Fusion Pooling (MVFP) module to precisely predict 3D objects. The
experimental results on KITTI 3D object detection benchmark dataset show that
our method achieves excellent detection performance improvement for a variety
of backbones, especially for low-performance backbones with the simple network
structures.
