Road surface reconstruction is crucial for autonomous driving, providing accurate and up-to-date road geometry for navigation, safety assessment, and infrastructure maintenance. Camera-based methods have become increasingly cost-effective and scalable for this task. However, achieving high-quality large-scale reconstruction remains challenging due to inconsistent observations of the same road surface points. These inconsistencies arise both within single sessions-caused by factors such as vehicle shadows and exposure shifts-and across multiple sessions, where changes in lighting conditions and viewpoints further exacerbate the problem. To address these challenges, we propose MS-Road, a camera-based approach for large-scale road surface reconstruction with strong geometric consistency. MS-Road leveraging self-supervised learning and multi-view consistent constrain to tackle two key issues: inconsistency in road appearance across different observations and inaccurate road height localization. By enforcing spatiotemporal consistency in both geometric and visual aspects, our method produces more reliable reconstructions within and across sessions. Experiments on two public datasets and a real-world dataset demonstrate that our approach achieves robust and high-fidelity reconstruction under diverse and challenging conditions.