Wang Guangming, Yang Yehui, Zhang Huixin, Liu Zhe, Wang Hesheng
IEEE Trans Cybern. 2022 Dec;52(12):13546-13556. doi: 10.1109/TCYB.2021.3124954. Epub 2022 Nov 18.
The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3-D convolution kernel to extract features from raw 3-D point clouds because of the unstructured property of point clouds. In this article, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3-D convolution operator. In addition, this article analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of the spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3-D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. The comparison experiments with the traditional grid-shaped 3-D convolution operator demonstrated that the newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. The source codes will be released on https://github.com/IRMVLab/SIConv.
点云的语义分割是机器人环境感知的重要组成部分。然而，由于点云的非结构化特性，直接采用传统的三维卷积核从原始三维点云中提取特征是困难的。在本文中，提出了一种球形插值卷积算子来取代传统的网格状三维卷积算子。此外，本文分析了基于距离作为插值权重的点云插值方法的缺陷，并通过结合距离和特征相关性提出了自学习距离特征密度。所提出的方法使球形插值卷积网络的特征提取更加合理有效。在点云的三维语义分割任务中证明了所提出网络的有效性。实验表明，该方法在ScanNet数据集和巴黎-里尔-3D数据集上取得了良好的性能。与传统网格状三维卷积算子的对比实验表明，新提出的特征提取算子提高了网络的精度并减少了网络的参数。源代码将在https://github.com/IRMVLab/SIConv上发布。
