LiDAR semantic segmentation frameworks predominantly leverage geometry-based features to differentiate objects within a scan. While these methods excel in scenarios with clear boundaries and distinct shapes, their performance declines in environments where boundaries are blurred, particularly in off-road contexts. To address this, recent strides in 3D segmentation algorithms have focused on harnessing raw LiDAR intensity measurements to improve prediction accuracy. Despite these efforts, current learning-based models struggle to correlate the intricate connections between raw intensity and factors such as distance, incidence angle, material reflectivity, and atmospheric conditions. Building upon our prior work, this paper delves into the advantages of employing calibrated intensity (also referred to as reflectivity) within learning-based LiDAR semantic segmentation frameworks. We initially establish that incorporating reflectivity as an input enhances the existing LiDAR semantic segmentation model. Furthermore, we present findings that enable the model to learn to calibrate intensity can boost its performance. Through extensive experimentation on the off-road dataset Rellis-3D, we demonstrate notable improvements. Specifically, converting intensity to reflectivity results in a 4% increase in mean Intersection over Union (mIoU) when compared to using raw intensity in Off-road scenarios. Additionally, we also investigate the possible benefits of using calibrated intensity in semantic segmentation in urban environments (SemanticKITTI) and cross-sensor domain adaptation.

翻译：激光雷达语义分割框架主要利用基于几何的特征来区分扫描中的物体。尽管这些方法在边界清晰、形状分明的场景中表现出色，但在边界模糊的环境中（尤其是越野场景）性能会下降。为解决这一问题，近期三维分割算法的进展聚焦于利用原始激光雷达强度测量值以提高预测精度。然而，现有基于学习的模型难以关联原始强度与距离、入射角、材料反射率及大气条件等复杂因素。基于我们先前的研究，本文深入探讨了在校准强度（即反射率）基础上，基于学习的激光雷达语义分割框架的优势。我们首先证明，将反射率作为输入可增强现有激光雷达语义分割模型。此外，研究结果表明，让模型学习校准强度可提升其性能。通过在越野数据集Rellis-3D上的广泛实验，我们取得了显著改进：在越野场景中，将强度转换为反射率后，与使用原始强度相比，平均交并比（mIoU）提升了4%。同时，我们也在城市环境（SemanticKITTI）及跨传感器域自适应中，探索了使用校准强度进行语义分割的潜在优势。