This paper investigates the impact of LiDAR configuration shifts on the performance of 3D LiDAR point cloud semantic segmentation models, a topic not extensively studied before. We explore the effect of using different LiDAR channels when training and testing a 3D LiDAR point cloud semantic segmentation model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained and tested on simulated 3D LiDAR point cloud datasets created using the Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64 channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a real-world off-road environment. Our experimental results demonstrate that sensor and spatial domain shifts significantly impact the performance of LiDAR-based semantic segmentation models. In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance. Moreover, higher-resolution sensors showed improved performance compared to those with lower-resolution ones. However, results varied when spatial domain changes were present. In some cases, the advantage of a sensor's higher resolution led to better performance both with and without sensor domain shifts. In other instances, the higher resolution resulted in overfitting within a specific domain, causing a lack of generalization capability and decreased performance when tested on data with different sensor configurations.

翻译：本文研究了LiDAR配置变化对3D LiDAR点云语义分割模型性能的影响，这是一个此前尚未被广泛探讨的课题。我们利用Cylinder3D进行实验，探究在训练和测试3D LiDAR点云语义分割模型时使用不同LiDAR通道的效果。一个Cylinder3D模型在基于密西西比州立大学自主车辆模拟器（MAVS）生成的模拟3D LiDAR点云数据集，以及真实越野环境RELLIS-3D数据集中收集的32通道和64通道3D LiDAR点云上进行了训练和测试。我们的实验结果表明，传感器域和空间域的变化显著影响了基于LiDAR的语义分割模型的性能。在训练和测试之间不存在空间域变化的情况下，使用同一传感器类型进行训练和测试的模型通常表现出更优的性能。此外，与分辨率较低的传感器相比，分辨率较高的传感器展现出更好的性能。然而，当存在空间域变化时，结果有所差异。在某些情况下，传感器较高分辨率的优势使其在存在和不存在传感器域变化的情况下均能获得更好的性能。在其他情况下，较高的分辨率会导致在特定域内过拟合，从而缺乏泛化能力，并在使用不同传感器配置的数据进行测试时性能下降。