Object detection in point clouds is a central aspect of many robotics applications such as autonomous driving. Real-time technologies require very high speed devices and demand low complexity algorithms, at the expense of accuracy. In this study we consider the trade-off between inference time and accuracy of an object detection model. In particular, our purpose is to answer the following question: how much can we reduce the inference time of said algorithm maintaining a sufficient accuracy and keep satisfactory performance? To solve this problem we exploit the PointPillars algorithm, an encoder that utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars), which outperforms many other methods with respect to both speed and accuracy by a large margin.