点云
计算机科学
分割
渡线
编码(社会科学)
人工智能
模式识别(心理学)
点(几何)
数据挖掘
算法
数学
统计
几何学
作者
Qihang Ma,Zh Jian,Jiahao Zhang
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2021-12-16
卷期号:41 (6): 7547-7562
摘要
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.
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