  LiDAR point clouds can effectively depict the motion and posture of objects
in three-dimensional space. Many studies accomplish the 3D object detection by
voxelizing point clouds. However, in autonomous driving scenarios, the sparsity
and hollowness of point clouds create some difficulties for voxel-based
methods. The sparsity of point clouds makes it challenging to describe the
geometric features of objects. The hollowness of point clouds poses
difficulties for the aggregation of 3D features. We propose a two-stage 3D
object detection framework, called MS23D. (1) We propose a method using voxel
feature points from multi-branch to construct the 3D feature layer. Using voxel
feature points from different branches, we construct a relatively compact 3D
feature layer with rich semantic features. Additionally, we propose a
distance-weighted sampling method, reducing the loss of foreground points
caused by downsampling and allowing the 3D feature layer to retain more
foreground points. (2) In response to the hollowness of point clouds, we
predict the offsets between deep-level feature points and the object's
centroid, making them as close as possible to the object's centroid. This
enables the aggregation of these feature points with abundant semantic
features. For feature points from shallow-level, we retain them on the object's
surface to describe the geometric features of the object. To validate our
approach, we evaluated its effectiveness on both the KITTI and ONCE datasets.
