From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation

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

Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst solution and perform anomaly detection on the dataset using a deep learning approach. The dataset contains a diverse set of anomalies with 11 identified common anomalies where the electrodes contain e.g. scratches, bubbles, smudges etc. We experiment with 16-bit image to 8-bit image conversion methods to utilize pre-trained Convolutional Neural Networks as feature extractors (transfer learning) and find that we achieve the best performance by maximizing the contrasts globally across the dataset during the 16-bit to 8-bit conversion, through histogram equalization. We group the fuel cell electrodes with anomalies into a single class called abnormal and the normal fuel cell electrodes into a class called normal, thereby abstracting the anomaly detection problem into a binary classification problem. We achieve a balanced accuracy of 85.18\%. The anomaly detection is used by the company, Serenergy, for optimizing the time spend on the quality control of the fuel cell electrodesComment: 10 pages, 9 figures, VISAPP202

Data-driven Hyperparameter Tuning for 3D Semantic Segmentation

Successfully applying state-of-the-art 3D semantic segmentation networks like PointNeXt to new datasets requires setting dataset-specific hyperparameters to suitable values. Specifically, the voxel grid size (for point cloud sampling) and query ball radius (for grouping) play a crucial role in many point-based architectures as they jointly determine the receptive field. Tuning these parameters via sweeping or trial-and-error is both time-consuming and computationally expensive. We therefore propose a training-free, data-driven method for automatically tuning the voxel grid size and query ball radius through a volumetric analysis of the training data. We demonstrate the effectiveness of the approach by evaluating the performance of PointNeXt with default parameters versus parameters set by our auto-tuning method across a diverse set of datasets: Beams&Hooks, ScanNetV2 and SemanticKITTI. Our method improves the mIoU score by 37.4, 0.5 and 26.3 percentage points, respectively, with negligible computational costs