We propose a fully automatic annotation scheme that takes a raw 3D point cloud with a set of fitted CAD models as input and outputs convincing point-wise labels that can be used as cheap training data for point cloud segmentation. Compared with 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 that are more difficult to label automatically is beneficial compared with 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，以原始3D点云及一组拟合的CAD模型为输入，输出具有说服力的逐点标签，可作为点云分割的低成本训练数据。与人工标注相比，我们的自动标签在显著减少标注时间、消除手动干预或数据集特定参数需求的同时，保持了标注精度。该标注流水线输出语义类别与软化的逐点目标得分，这些得分可二值化为标准独热编码标签、通过阈值处理为带有未标注模糊点的弱标签，或在训练过程中直接作为软标签使用。我们在真实工业点云数据集与室内场景公共数据集Scan2CAD上评估了标签质量及PointNet++的分割性能。结果表明：相比传统朴素地为每个点赋予硬性“最佳猜测”标签的方法，在自动标注难度较高的区域降低监督强度更为有效。