无需标注数据，利用几何与时间信息实现高精度道路分割。
Unsupervised Monocular Road Segmentation for Autonomous Driving via Scene Geometry
- 基于视野以上为非道路、前方四边形为道路的几何先验生成弱标签。
- 通过特征点追踪与互信息最大化，提升标签精度与时间稳定性。
- 在Cityscapes上达0.86 IoU，适合无标注数据场景的自动驾驶研究。
本文提出一种完全无监督的二值道路分割方法（道路 vs. 非道路），摆脱对昂贵人工标注数据集的依赖。该方法利用场景几何与时间线索区分道路与非道路区域。首先，通过几何先验生成弱标签：将视野以上的像素标记为非道路，将车辆前方预定义的四边形区域标记为道路。在精修阶段，通过跨帧追踪局部特征点，并使用互信息最大化惩罚不一致的标签分配，从而提升精度与时间稳定性。在Cityscapes数据集上，模型达到0.86的交并比（IoU），优于现有无监督方法。结果表明，结合几何约束与时间一致性，可实现可扩展的无监督道路分割，适用于自动驾驶场景。
原文摘要 · Abstract (English)
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 0.86, outperforming the competing unsupervised methods. These findings demonstrate the potential of combining geometric constraints and temporal consistency for scalable unsupervised road segmentation in autonomous driving.
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