  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.
