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。

MS23D：一种使用多尺度语义特征点构建3D特征层的三维目标检测方法