LiDAR-based 3D object detection relies on the relatively rich information captured by LiDAR point clouds. However, computational efficiency often requires the downsampling of these point clouds. This paper studies the impact of downsampling strategies on the robustness of a state-of-the-art object detector, namely PointPillars. We compare the performance of the approach under random sampling and farthest point sampling, evaluating the model’s accuracy in detecting objects across various downsampling ratios. The experiments were conducted on the popular KITTI dataset.