We propose a fully automatic annotation scheme which takes a raw 3D point cloud with a set of fitted CAD models as input, and outputs convincing point-wise labels which can be used as cheap training data for point cloud segmentation. Compared to manual annotations, we show that our automatic labels are accurate while drastically reducing the annotation time, and eliminating the need for manual intervention or dataset-specific parameters. Our labeling pipeline outputs semantic classes and soft point-wise object scores which can either be binarized into standard one-hot-encoded labels, thresholded into weak labels with ambiguous points left unlabeled, or used directly as soft labels during training. We evaluate the label quality and segmentation performance of PointNet++ on a dataset of real industrial point clouds and Scan2CAD, a public dataset of indoor scenes. Our results indicate that reducing supervision in areas which are more difficult to label automatically is beneficial, compared to the conventional approach of naively assigning a hard "best guess" label to every point.

翻译：我们提出了一种全自动标注方案，该方案以原始三维点云和一组拟合的CAD模型为输入，输出可用于点云分割廉价训练数据的可靠逐点标签。与人工标注相比，我们的自动标签在显著缩短标注时间的同时保持了准确性，并消除了人工干预或数据集特定参数的需求。我们的标注流程输出语义类别和逐点软目标分数，这些分数可以二值化为标准独热编码标签、阈值化为保留未标注模糊点的弱标签，或在训练过程中直接用作软标签。我们在真实工业点云数据集和室内场景公开数据集Scan2CAD上，评估了PointNet++的标签质量与分割性能。结果表明，与对所有点均朴素分配硬性“最优猜测”标签的传统方法相比，在自动标注难度较大的区域减少监督更为有利。