High-precision real-time 3D object detection based on the LiDAR point cloud is an important task for autonomous driving. Most existing methods utilize grid-based convolutional networks to handle sparse and cluttered point clouds. However, the performance of object detection is limited by the coarse grid quantization and expensive computational cost. In this paper, we propose a more efficient representation of 3D point clouds and propose SCNet, a single-stage, end-to-end 3D subdivision coding network that learns finer feature representations for vertical grids. SCNet divides each grid into smaller sub-grids to preserve more point cloud information and converts points in the grid to a uniform feature representation through 2D convolutional neural networks. The 3D point cloud is encoded as the fine 2D sub-grid representation, which helps to reduce the computational cost. We validate our SCNet on the KITTI object benchmark in which we show that the proposed object detector produces state-of-the-art results with more than 20 FPS.