In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.

该研究提出了一种处理LiDAR传感器数据的架构，通过将关联的投影属性编码到一个额外的通道中来解决传感器依赖的对象表示引起的偏差问题，并通过实验在控制的合成环境中验证方法可以降低传感器分辨率和视场的偏差，提高机器视觉任务的性能和鲁棒性。

激光雷达投影图像的传感器等变性