Multi-PointNet++: A Multi-Scale Local Interaction Network for Plant Point Cloud Segmentation

Abstract

To address the challenges of complex structures, large organ-scale variations, and dense internal organization in plant 3D point clouds, This paper proposes Multi-PointNet++ algorithm based on multi-scale local interaction to enhance the accuracy and detail extraction capability of plant point cloud segmentation. We first construct the EMA-Shuffle attention module during feature extraction, combining Enhanced Multiscale Attention (EMA) with Shuffle Attention to enable efficient local feature interaction through multi-scale fusion. At the same time, the OREPA reparameterized convolution module enhances feature extraction and model performance while keeping the structure lightweight. For feature fusion, we design a pyramid fusion module based on SFPN that integrates triple sampling strategies and bidirectional feature propagation to effectively align geometric information across scales. Our method improves mIoU by 7.1% and 5.3%, and mAcc by 6.7% and 13.7% on the public PLANesT-3D and self-built sunflower datasets, respectively, significantly outperforming other models. This approach offers robust 3D semantic support for high-throughput plant phenotype extraction.

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