Abstract—Light Detection and Ranging (LiDAR) sensors play a crucial role in autonomous driving, specially in HDMaps generation systems, which contain semantic information of the road scenarios. Gathering this information is not easy, so in the literature deep learning-based semantic segmentation on LiDAR point clouds is used to accelerate this process. However, it requires large amounts of data for training, which are time-consuming and computationally intensive to obtain. In this paper, we introduce a method to use the synthetic dataset KITTI-CARLA to reduce the amount of real data needed to train a Deep Learning-based model on SemanticKITTI. The main challenge is that in KITTI-CARLA the intensity field is not computed. To overcome this, we define a statistic module that infer the real distribution of intensities of SemanticKITTI into KITTI-CARLA. We tested our method with the Point-to-Voxel-KD model. Extensive experiments show that results near the state of the art can be obtained with half the amount of real data when KITTI-CARLA is incorporated to the training set. The source code for the intensity distribution calculation and the conversion process from KITTI-CARLA to SemanticKITTI is published in https://github.com/pabloaguilarp/KITTI-CARLA-converter.
 
Keywords—Light Detection and Ranging (LiDAR), deep learning, semantic segmentation, simulation


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