AI 中文总结
针对商业猪舍中猪只点云分割的边界模糊等问题，采用Octree Transformer骨干网络，结合软距离边界伪标签与双向跨边界语义模块，提升分割性能，为精准畜牧养殖提供可靠输入。
AI 中文摘要
在真实的猪舍环境中，猪只点云常与背景结构紧密接触，导致目标边界模糊、局部粘连以及背景误分割，这会降低后续点云补全和体型测量的精度。为应对这些挑战，本研究提出一种基于边界特征分析的猪只点云分割方法。该方法采用Octree Transformer作为骨干网络，通过八叉树卷积、自注意力编码和多尺度特征融合，将局部几何细节与全局语义上下文相结合；此外，生成软距离边界伪标签以提供连续的边界监督，并设计双向跨边界语义模块，实现边界特征与语义特征的显式交互。在综合数据集上开展的实验表明，所提方法在分割精度、平均交并比及边界描绘方面显著优于多种最先进模型，结果显示该方法可有效缓解边界粘连问题，为下游精准畜牧养殖任务提供可靠的点云输入。
英文摘要
In real pigsty environments, pig point clouds often come into close contact with background structures, resulting in blurred target boundaries, local adhesion, and background mis-segmentation. This reduces the accuracy of subsequent point cloud completion and body size measurement. To address these challenges, this study proposes a pig point cloud segmentation method based on boundary feature analysis. The proposed method adopts Octree Transformer as the backbone network and integrates local geometric details with global semantic context through octree convolution, self-attention encoding, and multi-scale feature fusion. Furthermore, soft-distance boundary pseudo-labels are generated to provide continuous boundary supervision, and a bidirectional cross-boundary semantic module is designed to enable explicit interaction between boundary and semantic features. Experiments conducted on a comprehensive dataset demonstrate that the proposed method significantly outperforms various state-of-the-art models in terms of segmentation accuracy, mean intersection over union, and boundary delineation. The results indicate that the method effectively alleviates boundary adhesion, providing reliable point cloud inputs for downstream precision livestock farming tasks.
Comments24 pages,9 figures, 5 tables
