Lidar point clouds, as a type of data with accurate distance perception, can
effectively represent the motion and posture of objects in three-dimensional
space. However, the sparsity and disorderliness of point clouds make it
challenging to extract features directly from them. Many studies have addressed
this issue by transforming point clouds into regular voxel representations.
However, these methods often lead to the loss of fine-grained local feature
information due to downsampling. Moreover, the sparsity of point clouds poses
difficulties in efficiently aggregating features in 3D feature layers using
voxel-based two-stage methods. To address these issues, this paper proposes a
two-stage 3D detection framework called MS$^{2}$3D. In MS$^{2}$3D, we utilize
small-sized voxels to extract fine-grained local features and large-sized
voxels to capture long-range local features. Additionally, we propose a method
for constructing 3D feature layers using multi-scale semantic feature points,
enabling the transformation of sparse 3D feature layers into more compact
representations. Furthermore, we compute the offset between feature points in
the 3D feature layers and the centroid of objects, aiming to bring them as
close as possible to the object's center. It significantly enhances the
efficiency of feature aggregation. To validate the effectiveness of our method,
we evaluated our method on the KITTI dataset and ONCE dataset together.

MS$^{2}$3D 是一个两阶段的三维检测框架，利用小尺寸的体素提取细粒度的局部特征和大尺寸的体素捕捉长程局部特征，通过多尺度语义特征点构建三维特征层并计算特征点与目标质心的偏移，以提高特征聚合的效率，在 KITTI 数据集和 ONCE 数据集上验证了该方法的有效性。