GSECnet: Ground Segmentation of Point Clouds for Edge Computing

TL;DR

GSECnet is an efficient ground segmentation framework for point clouds designed for low-power edge devices, balancing high accuracy with low computational complexity and real-time performance.

Contribution

The paper introduces GSECnet, a novel point cloud segmentation method optimized for deployment on low-power edge computing units.

Findings

Achieves 135.2 Hz inference speed on desktop

Operates effectively on a 10-watt low-power edge device

Balances high accuracy with low computational complexity

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.