In this work, we target the problem of uncertain points refinement for image-based LiDAR point cloud semantic segmentation (LiDAR PCSS). This problem mainly results from the boundary-blurring problem of convolution neural networks (CNNs) and quantitation loss of spherical projection, which are often hard to avoid for common image-based LiDAR PCSS approaches. We propose a plug-and-play transformer-based uncertain point refiner (TransUPR) to address the problem. Through local feature aggregation, uncertain point localization, and self-attention-based transformer design, TransUPR, integrated into an existing range image-based LiDAR PCSS approach (e.g., CENet), achieves the state-of-the-art performance (68.2% mIoU) on Semantic-KITTI benchmark, which provides a performance improvement of 0.6% on the mIoU.

翻译：针对图像式激光雷达点云语义分割（LiDAR PCSS）中的不确定点优化问题，本文提出了一种即插即用的基于Transformer的不确定点优化器（TransUPR）。该问题主要源于卷积神经网络（CNNs）的边界模糊效应以及球面投影的量化损失——这两类常见于图像式LiDAR PCSS方法的固有缺陷。通过局部特征聚合、不确定点定位及基于自注意力的Transformer架构设计，将TransUPR嵌入现有基于距离图的LiDAR PCSS方法（如CENet）后，在Semantic-KITTI基准上实现了当前最优性能（68.2% mIoU），较原始方法提升0.6% mIoU精度。