Unsupervised Monocular Road Segmentation for Autonomous Driving via Scene Geometry
This addresses the need for scalable, cost-effective road segmentation in autonomous driving, though it is incremental in combining existing techniques.
The paper tackles the problem of road segmentation for autonomous driving without manual labels by using scene geometry and temporal cues, achieving an IoU of 0.82 on Cityscapes.
This paper presents a fully unsupervised approach for binary road segmentation (road vs. non-road), eliminating the reliance on costly manually labeled datasets. The method leverages scene geometry and temporal cues to distinguish road from non-road regions. Weak labels are first generated from geometric priors, marking pixels above the horizon as non-road and a predefined quadrilateral in front of the vehicle as road. In a refinement stage, temporal consistency is enforced by tracking local feature points across frames and penalizing inconsistent label assignments using mutual information maximization. This enhances both precision and temporal stability. On the Cityscapes dataset, the model achieves an Intersection-over-Union (IoU) of 0.82, demonstrating high accuracy with a simple design. These findings demonstrate the potential of combining geometric constraints and temporal consistency for scalable unsupervised road segmentation in autonomous driving.