Semantic Segmentation Network of Noisy Plant Point Cloud based on Self-Attention Feature Fusion

When 3D reconstruction of plant seedlings is performed to obtain plant point clouds, there are many noisy points between the leaves and stems of plant point clouds due to the influence of ambient light and the limitation of the camera vision, which affects the automatic measurement of plant phenotypes.In order to achieve plant point cloud stem and leaf segmentation with a large amount of noise, We propose a network for semantic segmentation of noisy plant point clouds based on self-attentive feature fusion (abbreviated as SAFF-Net).The network first extracts shallow spatial features and higher-level semantic features between points by using correlations between pairs of points in the neighborhood through the local feature fusion module, and enhances the ability to extract plant shape features;The dual-branch attention pooling module is used to aggregate features, and one branch uses the channel attention mechanism to adaptively filter low-correlation features, avoiding redundancy of features and mitigating the bias effect of noise points.Another branch performs max pooling for the highest level feature map to obtain global contextual features, and finally merges local and global contextual features to learn more discriminative features.The point cloud dataset for the experiment is derived from multi-angle photographs of the plant taken by a high-definition camera and reconstructed in 3D by Structure from Motion(SFM) algorithm.The experimental results indicate that SAFF-Net performs better than the mainstream semantic segmentation networks and extracts more fine-grained local features. OA and (mIoU) of this method on the plant dataset are 93.7% and 83.4%, respectively. It outperforms existing methods in the segmentation of noise points and leaves, and the segmentation results have higher precision and recall.