Deep Unsupervised Segmentation of Log Point Clouds
This work addresses the need for cost-efficient and fast log measurement in sawmills, offering an incremental improvement over existing methods by introducing an unsupervised approach for cylindrical object segmentation.
The paper tackles the problem of segmenting wooden log surface point clouds for sawmill optimization by proposing a novel Point Transformer-based unsupervised segmentation technique that leverages geometric properties of cylinders, achieving accurate segmentation as demonstrated on wooden logs.
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.