  In this paper, we propose PASS3D to achieve point-wise semantic segmentation
for 3D point cloud. Our framework combines the efficiency of traditional
geometric methods with robustness of deep learning methods, consisting of two
stages: At stage-1, our accelerated cluster proposal algorithm will generate
refined cluster proposals by segmenting point clouds without ground, capable of
generating less redundant proposals with higher recall in an extremely short
time; stage-2 we will amplify and further process these proposals by a neural
network to estimate semantic label for each point and meanwhile propose a novel
data augmentation method to enhance the network's recognition capability for
all categories especially for non-rigid objects. Evaluated on KITTI raw
dataset, PASS3D stands out against the state-of-the-art on some results, making
itself competent to 3D perception in autonomous driving system. Our source code
will be open-sourced. A video demonstration is available at
https://www.youtube.com/watch?v=cukEqDuP_Qw.
