In recent years, computer vision has transformed fields such as medical imaging, object recognition, and geospatial analytics. One of the fundamental tasks in computer vision is semantic image segmentation, which is vital for precise object delineation. Autonomous driving represents one of the key areas where computer vision algorithms are applied. The task of road surface segmentation is crucial in self-driving systems, but it requires a labor-intensive annotation process in several data domains. The work described in this paper aims to improve the efficiency of image segmentation using a convolutional neural network in a multi-sensor setup. This approach leverages lidar (Light Detection and Ranging) annotations to directly train image segmentation models on RGB images. Lidar supplements the images by emitting laser pulses and measuring reflections to provide depth information. However, lidar's sparse point clouds often create difficulties for accurate object segmentation. Segmentation of point clouds requires time-consuming preliminary data preparation and a large amount of computational resources. The key innovation of our approach is the masked loss, addressing sparse ground-truth masks from point clouds. By calculating loss exclusively where lidar points exist, the model learns road segmentation on images by using lidar points as ground truth. This approach allows for blending of different ground-truth data types during model training. Experimental validation of the approach on benchmark datasets shows comparable performance to a high-quality image segmentation model. Incorporating lidar reduces the load on annotations and enables training of image-segmentation models without loss of segmentation quality. The methodology is tested on diverse datasets, both publicly available and proprietary. The strengths and weaknesses of the proposed method are also discussed in the paper.

翻译：摘要：近年来，计算机视觉已深刻变革医学成像、物体识别和地理空间分析等领域。语义图像分割是计算机视觉的基础任务之一，对于精确的物体轮廓勾勒至关重要。自动驾驶是计算机视觉算法应用的关键领域之一，其中路面分割任务尤为重要，但需要在多个数据域中进行耗费大量人力的标注过程。本文旨在利用多传感器配置下的卷积神经网络提升图像分割效率。该方法直接利用激光雷达（光探测与测距）标注来训练基于RGB图像的图像分割模型。激光雷达通过发射激光脉冲并测量反射信号补充图像的深度信息。然而，激光雷达稀疏的点云常给精确的物体分割带来困难。点云分割需要耗时的数据预处理和大量计算资源。该方法的核心创新在于引入掩码损失函数，以处理点云生成的稀疏真实标注掩码。通过仅在激光雷达点存在的区域计算损失，模型将激光雷达点作为真实标注来学习图像中的路面分割。这种方法允许在模型训练中融合不同类型的真实标注数据。在基准数据集上的实验验证表明，该方法达到了与高质量图像分割模型相当的性能。集成激光雷达可减少标注工作量，并在不损失分割质量的前提下实现图像分割模型的训练。该方法已在公开和专有等多种数据集上得到测试。文中还讨论了所提出方法的优势与不足。