2025
doi: 10.12720/jait.16.2.223-232
LiDAR Data Augmentation for Semantic Segmentation: A Statistical Intensity Model
Pablo Aguilar-Pérez1,2,*
,
Rebeca P. Díaz-Redondo1,*
- 1atlanTTic Research Center, Information & Computing Lab. Telecommunication Engineering School, Universidade de Vigo. Vigo, 36310, Spain
- 2Centro Tecnológico de Automoción de Galicia, Avenida Principal, 2, 36475 O Porriño, Pontevedra, Spain
- Manuscript receivedAugust 23, 2024
- revisedSeptember 26, 2024
- acceptedNovember 12, 2024
- publishedFebruary 17, 2025
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
How to Cite
Pablo Aguilar-Pérez and Rebeca P. Díaz-Redondo, "LiDAR Data Augmentation for Semantic Segmentation: A Statistical Intensity Model," Journal of Advances in Information Technology, vol. 16, no. 2, pp. 223-232, 2025. https://doi.org/10.12720/jait.16.2.223-232
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
