From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation
This addresses the annotation bottleneck for 3D semantic segmentation in industrial and indoor applications, offering a practical, incremental improvement over existing methods.
The paper tackles the problem of expensive manual annotation for 3D point cloud segmentation by proposing an automatic pipeline that generates cheap, accurate labels from CAD models, reducing annotation time and eliminating manual intervention. Results show that using soft labels or thresholding ambiguous points improves segmentation performance on industrial and indoor datasets compared to hard labeling.
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.