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SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds
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- Authors
- Ki-In Na, Byungjae Park, Jong-Hwan Kim
- Issue Date
- 2020-10
- Citation
- International Conference on Systems, Man, and Cybernetics (SMC) 2020, pp.1-6
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- 3D point cloud, Auto-Encoder(AE), Bounding Box, Convolutional auto-encoder, Fast and accurate, Geometric features, Object segmentation, Region Growing Algorithm, Semantic segmentation, Spherical projection, Well-balanced