Pedestrian detection is an important problem for autonomous driving. It is still chanllenging to detect and segment pedestrians from point clouds. In this paper, we propose a method named SurfaceNet to detect and segment pedestrians from point clouds. Specifically, we propose a novel representation, named surface map, to represent a point cloud as a 2D pseudo-image. For pedestrian detection, the proposed method comprises of four modules: 1) a grid feature encoder that can processes arbitrary number of points within each grid; 2) a surface feature convolutional module that employs a set of 2D convolutional layers to extract high level features; 3) a view transform module that transforms features from front view to bird's eye view; and 4) an anchor-free 3D object detection head that produces rotated 3D bounding box predictions. For semantic segmentation, the 2D pseudo-image is used for semantic segmentation and the segmentation results are re-projected to the original point cloud to achieve point cloud segmentation. Experimental results on the KITTI dataset show that our method achieves promising performance on pedestrian detection and segmentation in point clouds.