# What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation Source: [What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:43:54.162Z Authors: Frederik Hasecke, Martin Alsfasser, Anton Kummert Published: 2022-06-20T09:10:59.000Z ## Abstract To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data. In this paper we present novel point cloud augmentation methods to artificially diversify a dataset. Our sensor-centric methods keep the data structure consistent with the lidar sensor capabilities. Due to these new methods, we are able to enrich low-value data with high-value instances, as well as create entirely new scenes. We validate our methods on multiple neural networks with the public SemanticKITTI dataset and demonstrate that all networks improve compared to their respective baseline. In addition, we show that our methods enable the use of very small datasets, saving annotation time, training time and the associated costs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on SemanticKITTI hidden (test)](): Semantic segmentation; SemanticKITTI hidden (test); mIoU: 65.4. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()