Research on denoising and segmentation algorithm application of pigs' point cloud based on DBSCAN and PointNet

At present, 3D vision technology has been more and more applied in the field of livestock breading industry. Three depth cameras are installed in the data acquisition walkway from different views, as the animals pass through the walkway, the depth cameras will get the local point cloud of livestock...

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Vydáno v:2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) s. 42 - 47
Hlavní autoři: Lin, Runheng, Hu, Hao, Wen, Zhikun, Yin, Ling
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.11.2021
Témata:
Cluster
Clustering algorithms
Deep Learning
Neural networks
Point cloud compression
Point Cloud Segmentation
PointNet
Production
Tagging
Three-dimensional displays
Training
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Popis
Shrnutí:At present, 3D vision technology has been more and more applied in the field of livestock breading industry. Three depth cameras are installed in the data acquisition walkway from different views, as the animals pass through the walkway, the depth cameras will get the local point cloud of livestock at the same time. By this method the livestock point cloud exists inevitable noise, as well as the background point cloud including railing and ground. Therefore, the segmentation of livestock point cloud from the original point cloud mixed with noise and background is the important first step of the application of three-dimensional vision in livestock breading industry. This paper proposed using the PointNet++ model to separate the target point cloud from the background point cloud. The improved DBSCAN clustering algorithm is proposed to denoise and segment the data preliminarily, which is helpful to label the data manually. The labeled data are used to train the PointNet++ neural network model to extract target point cloud efficiently and accurately. The experimental results show that the mean Intersection over Union (MIOU) of pig target point cloud segmentation based on single-scale grouping and multi-scale grouping Pointnet++ are 0.932 and 0.940, respectively, which both obtain good segmentation results.
DOI:10.1109/MetroAgriFor52389.2021.9628501