Object Insertion Based Data Augmentation for Semantic Segmentation

Yuan Ren, Siyan Zhao, Bingbing Liu

Abstract: Neural network used for the LiDAR semantic segmentation task needs the point-wise labeled point clouds for training, which is more expensive than bounding box annotations. Enhancing the diversity of training data through object insertion is an effective method to reduce labeling costs. The existing object insertion methods are mainly divided into two categories. First is “copy” the clusters from a LiDAR frame and “paste” it to other frames or positions. Second is inserting CAD models into the background then using LiDAR simulator to generate laser points of the inserted CAD models. “Copy-paste” method cannot generate realistic scanning lines and shadows, and the CAD models, especially the CAD models of flexible objects, are hard to obtain. We propose an object insertion based data augmentation method which can increase the performance of the semantic segmentation network remarkably. First, an object library is created by using the labeled LiDAR point clouds. Then, these objects are inserted into the LiDAR point clouds dynamically during the training. Finally, the realistic scanning lines and shadows are simulated according to the real LiDAR parameters. The experimental results show that the proposed augmentation method can increase the performance of different semantic segmentation frameworks remarkably.