Accurate detection of objects in 3D point clouds is a central problem for autonomous navigation. Most existing methods use techniques of hand-crafted features representation or multi-sensor approaches prone to sensor failure. Approaches like PointNet that directly operate on sparse point data have shown good accuracy in the classification of single 3D objects. However, LiDAR sensors on Autonomous Vehicles generate a large scale point cloud. Real-time object detection in such a cluttered environment still remains a challenge. In this study, we propose Attentional PointNet, which is a novel end-to-end trainable deep architecture for object detection in point clouds. We extend the theory of visual attention mechanisms to 3D point clouds and introduce a new recurrent 3D Localization Network module. Rather than processing the whole point cloud, the network learns where to look (finding regions of interest), which significantly reduces the number of points to be processed and inference time. Evaluation on KIM car detection benchmark shows that our Attentional PointNet achieves comparable results with the state-of-the-art LiDAR-based 3D detection methods in detection and speed.