PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
This work addresses 3D perception for autonomous driving systems, presenting an incremental improvement through a hybrid approach.
The paper tackles 3D point cloud semantic segmentation by proposing PASS3D, a two-stage framework combining geometric and deep learning methods, which achieves state-of-the-art results on the KITTI raw dataset for autonomous driving applications.
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.