Active sensors such as LiDARs (light detection and ranging) are popular in autonomous driving systems for perception and localization. Existing perception approaches process the rich 3D LiDAR point clouds for object detection, tracking and recognition. These methods generally require an initial segmentation procedure containing two steps: (1) filter points as ground and non-ground points, and (2) cluster the non-ground points into objects. Leveraging a range image created in Spherical coordinates, this paper proposes a field-tested coarse-to-fine 3D point cloud segmentation framework to achieve both speed and accuracy. Under this framework, a basic version and an advanced version of segmentation algorithms are presented. In the basic version, we demonstrate how a coarse-to-fine scheme is applied in a range image for ground filtering and object clustering. In the advanced version, we move forward to reduce the processing time and correct the motion distortion by directly dealing with the data packets instead of the range image. Tests in the well-known KITTI dataset and field experiments in public roads have shown that the method significantly improves the speed of 3D point cloud segmentation whilst maintains good accuracy.