GSECnet: Ground Segmentation of Point Clouds for Edge Computing

• URL: http://arxiv.org/abs/2104.01766v1
• Date: Mon, 5 Apr 2021 04:29:28 GMT
• Title: GSECnet: Ground Segmentation of Point Clouds for Edge Computing
• Authors: Dong He, Jie Cheng, Jong-Hwan Kim
• Abstract summary: GSECnet is an efficient ground segmentation framework designed to be deployable on a low-power edge computing unit. Our framework achieves the runtime inference of 135.2 Hz on a desktop platform.
• Score: 7.481096704433562
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Ground segmentation of point clouds remains challenging because of the sparse and unordered data structure. This paper proposes the GSECnet - Ground Segmentation network for Edge Computing, an efficient ground segmentation framework of point clouds specifically designed to be deployable on a low-power edge computing unit. First, raw point clouds are converted into a discretization representation by pillarization. Afterward, features of points within pillars are fed into PointNet to get the corresponding pillars feature map. Then, a depthwise-separable U-Net with the attention module learns the classification from the pillars feature map with an enormously diminished model parameter size. Our proposed framework is evaluated on SemanticKITTI against both point-based and discretization-based state-of-the-art learning approaches, and achieves an excellent balance between high accuracy and low computing complexity. Remarkably, our framework achieves the inference runtime of 135.2 Hz on a desktop platform. Moreover, experiments verify that it is deployable on a low-power edge computing unit powered 10 watts only.

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