Search papers, labs, and topics across Lattice.
This study introduces a novel segmentation method for pig point clouds in complex housing environments, addressing issues like blurred boundaries and background mis-segmentation that hinder accurate body size measurements. By leveraging an Octree Transformer backbone and integrating local geometric details with global semantic context, the method enhances segmentation accuracy through innovative boundary feature analysis. Experimental results show that this approach significantly outperforms existing models, improving mean intersection over union and boundary delineation, which is crucial for precision livestock farming applications.
Boundary adhesion in pig point clouds is effectively mitigated, leading to a substantial boost in segmentation accuracy for precision livestock farming.
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.
