Boundary-Enhanced Segmentation of Pig Point Clouds in Commercial Housing Environments
This work provides a domain-specific improvement for precision livestock farming, enabling more reliable point cloud processing for automated pig monitoring, though it is incremental in methodology.
The paper addresses the problem of inaccurate pig point cloud segmentation in commercial housing environments due to blurred boundaries and background adhesion. The proposed boundary-enhanced method, using Octree Transformer and boundary supervision, significantly outperforms state-of-the-art models in segmentation accuracy and boundary delineation, improving downstream tasks like body size measurement.
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.