SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds (SMC 2020)
This repository contains brief description and dataset link for our full paper at SMC 2020 "SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds". We have the plan to open the code, but The code is not opened yet. If you have interest in our paper, please contact to author(kina4147@etri.re.kr) and cite our papers:
@article{Na2020SMC_SemanticPriorSeg,
author = {Ki-In Na, Byungjae Park and Jong-Hwan Kim},
journal = {SMC},
title = {SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds},
year = {2020}
}
Detection and classification of road-objects like cars, pedestrians, and cyclists is the first step in autonomous driving. In particular, point-wise object segmentation for 3D point clouds is essential to estimate the precise appearances of the road-objects. In this paper, we propose SPriorSeg, a fast and accurate point-level object segmentation for point clouds by integrating the strengths of deep convolutional auto-encoder and region growing algorithm. Semantic segmentation using the light-weighted convolutional auto-encoder generates semantic prior by labeling a spherical projection image of point clouds pixel-by-pixel with classes of road-objects. The region growing algorithm achieves pixel-wise instance segmentation by taking into account semantic prior and geometric features between neighboring pixels. We build a well-balanced, pixel-level labeled dataset for all classes using 3D bounding boxes and point clouds from the KITTI object dataset. The dataset is employed to train our light-weighted neural network for semantic segmentation and demonstrate the performance of both semantic and instance segmentation of SPriorSeg.
We built our dataset including pixel-wise semantic and instance labeled channels from the KITTI object dataset through the same process as described in SqueezeSeg. Our dataset is well-balanced for all the classes: a pedestrian, car, and cyclist. 3D bounding boxes of the KITTI object dataset are annotated on the front camera image, and the 3D point cloud is from Velodyne HDL-64E LiDAR with 64 channels. Therefore, the dataset has 512 grids between −45deg and 45deg in the row direction and 64 grids between −24.8deg and 2deg in the column direction. You can download dataset here
This work was supported by the ICT R&D program of MSIP/IITP. [2017-0-00306, Development of Multimodal Sensor-based Intelligent Systems for Outdoor Surveillance Robots]