Local information coding helps capture the fine-grained features of the point cloud. The point cloud coding mechanism should be applicable to the point cloud data in different formats. However, the local features of the point cloud are directly affected by the attributes, size and scale of the object. This paper proposes an Adaptive Locally-Coded point cloud classification and segmentation Network coupled with Genetic Algorithm(ALCN-GA), which can automatically adjust the size of search cube to complete network training. ALCN-GA can adapt to the features of 3D data at different points, whose adjustment mechanism is realized by designing a robust crossover and mutation strategy. The proposed method is tested on the ModelNet40 dataset and S3DIS dataset. Respectively, the overall accuracy and average accuracy is 89.5% and 86.5% in classification, and overall accuracy and mIoU of segmentation is 80.34% and 51.05%. Compared with PointNet, average accuracy in classification and mIoU of segmentation is improved about 10% and 11% severally.

中文翻译：

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结合遗传算法的自适应局部编码点云分类分割网络

局部信息编码有助于捕获点云的细粒度特征。点云编码机制应适用于不同格式的点云数据。然而，点云的局部特征直接受到对象的属性、大小和尺度的影响。本文提出了一种自适应Locally-Coded点云分类分割网络结合遗传算法(ALCN-GA)，可以自动调整搜索立方体的大小来完成网络训练。ALCN-GA 可以适应 3D 数据在不同点的特征，其调整机制是通过设计稳健的交叉和变异策略来实现的。所提出的方法在 ModelNet40 数据集和 S3DIS 数据集上进行了测试。分类的总体准确率和平均准确率分别为89.5%和86.5%，分割的整体准确率和 mIoU 分别为 80.34% 和 51.05%。与PointNet相比，分类的平均准确率和分割的mIoU分别提高了10%和11%左右。