3D object detection in point cloud data is an important aspect of computer vision systems, especially for autonomous driving applications. Recent literature suggests two methods of point cloud encoders; grid-based methods tend to be fast but sacrifice accuracy, while point-based methods that are learned from raw data are more accurate, but slower. In this work, we present a novel and real-time two-stage 3D object detection framework, named PointPillars-RCNN (PP-RCNN). In the first stage, we use pillars network to encode the point cloud and generate high-qulaity 3D proposals. Benefiting from the pillars network, our framework realizes real-time detection. In the second stage, we use the Point-Pillars Feature Set Abstraction (PPSA) module to extract the point-based features from raw point cloud and pillars features, and then we use the RoI-grid feature abstraction for proposals refinement. All our detection pipelines are trained end-to-end. Extensive experiments on the KITTI benchmark shows that our approach has better performance than the one-stage PointPillars algorithm, and faster than current two-stage state-of-the-art algorithms.