Voxel-based method is usually used in autonomous driving when conducting three-dimensional (3D) object detection based on a point cloud. This method is associated with small computational complexity and small latency. However, the current algorithms used in the industry often result in double information loss. Voxelization can bring information loss of point cloud. In addition, these algorithms do not entirely utilize the point cloud information after voxelization. Thus, this study designs a three-stage network to solve the problem of large information loss. In the first stage, an excellent voxel-based algorithm is used to output the proposal bounding box. In the second stage, the information on the feature map associated with the proposal is used to refine the bounding box, which aims to solve the problem of insufficient information utilization. The third stage uses the precise location of the original points, which make up for the information loss caused by voxelization. On the Waymo Open Dataset, the detection accuracy of the proposed multistage 3D object detection method is better than CenterPoint and other excellent algorithms favored by the industry. Meanwhile, it meets the requirement of latency for autonomous driving.