Boundary-Enhanced Segmentation of Pig Point Clouds in Commercial Housing Environments

2026-08-12

AI Summary

This study addresses the challenges of ambiguous boundaries, local adhesion, and erroneous segmentation between pigs and background in commercial pig barns by proposing a boundary-aware point cloud segmentation method. The approach employs an Octree Transformer backbone to effectively integrate fine-grained local geometric details with global semantic context. It introduces soft-distance boundary pseudo-labels for continuous boundary supervision and incorporates a novel bidirectional cross-boundary semantic module to explicitly model interactions between boundary cues and semantic features. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art models on a comprehensive dataset, achieving superior performance in segmentation accuracy, mean Intersection over Union (mIoU), and boundary delineation, thereby providing high-quality point cloud inputs for precision livestock farming.

Abstract

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.

Problem

• point cloud segmentation
• boundary ambiguity
• background adhesion
• precision livestock farming
• 3D boundary delineation

Innovation

• Boundary-aware segmentation
• Octree Transformer
• Soft-distance pseudo-labels
• Cross-boundary semantic interaction
• Point cloud segmentation