SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging
This provides a cost-effective alternative for researchers and developers in autonomous driving and perception systems, though it is incremental as it builds on existing simulation tools and datasets like SemanticKITTI.
The authors tackled the problem of expensive and time-consuming real LiDAR data collection for semantic segmentation by creating SynthmanticLiDAR, a synthetic dataset using a modified CARLA simulator, and showed that incorporating it into training improves algorithm performance.
Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI have been manually collected and labeled, the introduction of simulation tools such as CARLA, has enabled the creation of synthetic datasets on demand. In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution. Using this tool, we have generated SynthmanticLiDAR, a synthetic dataset for semantic segmentation on LiDAR imaging, designed to be similar to SemanticKITTI, and we evaluate its contribution to the training process of different semantic segmentation algorithms by using a naive transfer learning approach. Our results show that incorporating SynthmanticLiDAR into the training process improves the overall performance of tested algorithms, proving the usefulness of our dataset, and therefore, our adapted CARLA simulator. The dataset and simulator are available in https://github.com/vpulab/SynthmanticLiDAR.