Manually labelling point cloud scenes for use as training data in machine learning applications is a time- and labour-intensive task. In this paper, we aim to reduce the effort associated with learning semantic segmentation tasks by introducing a semi-supervised method that operates on scenes with only a small number of labelled points. For this task, we advocate the use of pseudo-labelling in combination with PointNet, a neural network architecture for point cloud classification and segmentation. We also introduce a method for incorporating information derived from spatial relationships to aid in the pseudo-labelling process. This approach has practical advantages over current methods by working directly on point clouds and not being reliant on predefined features. Moreover, we demonstrate competitive performance on scenes from three publicly available datasets and provide studies on parameter sensitivity.

中文翻译：

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稀疏注释的3D点云上的伪标记辅助语义分割

手动标记点云场景以用作机器学习应用程序中的训练数据是一项耗时且费力的任务。在本文中，我们旨在通过引入一种在仅带有少量标记点的场景上运行的半监督方法，来减少与学习语义分割任务相关的工作量。对于此任务，我们提倡将伪标签与PointNet结合使用，PointNet是一种用于点云分类和分段的神经网络体系结构。我们还介绍了一种合并来自空间关系的信息以辅助伪标记过程的方法。通过直接在点云上工作而不依赖于预定义的特征，该方法相对于当前方法具有实际优势。此外，