无需标注数据，用点云自动分割木头表面，提升锯切效率。
Deep Unsupervised Segmentation of Log Point Clouds
- 基于点变换器，利用圆柱几何特性无监督学习分割点云。
- 在真实木料数据上达到高精度，可识别复杂曲面与形变。
- 适合木材工业、3D重建或类似圆柱物体的自动化分析。
在锯木厂中，准确测量原材料——原木——对于优化锯切过程至关重要。先前研究显示，仅通过激光扫描生成的表面点云即可准确预测原木内部结构，这为成本更低、速度更快的替代方案提供了可能，取代了依赖X射线断层扫描的设备。分析原木点云的关键步骤是分割，因为它是发现精细表面特征的基础，这些特征能提供关于原木内部结构的线索。本文提出一种基于点变换器的新型点云分割技术，以无监督方式学习识别属于原木表面的点。该方法通过一个利用圆柱几何特性的损失函数实现，同时考虑了木材原木中常见的形状变化。我们在真实木料数据上验证了该方法的准确性，但该方法也可用于其他圆柱形物体。
原文摘要 · Abstract (English)
In sawmills, it is essential to accurately measure the raw material, i.e. wooden logs, to optimise the sawing process. Earlier studies have shown that accurate predictions of the inner structure of the logs can be obtained using just surface point clouds produced by a laser scanner. This provides a cost-efficient and fast alternative to the X-ray CT-based measurement devices. The essential steps in analysing log point clouds is segmentation, as it forms the basis for finding the fine surface details that provide the cues about the inner structure of the log. We propose a novel Point Transformer-based point cloud segmentation technique that learns to find the points belonging to the log surface in unsupervised manner. This is obtained using a loss function that utilises the geometrical properties of a cylinder while taking into account the shape variation common in timber logs. We demonstrate the accuracy of the method on wooden logs, but the approach could be utilised also on other cylindrical objects.
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