Accurate surround-view depth estimation provides a competitive alternative to laser-based sensors and is essential for 3D scene understanding in autonomous driving. While empirical studies have proposed various approaches that primarily focus on enforcing cross-view constraints at photometric level, few explicitly exploit the rich geometric structure inherent in both monocular and surround-view setting. In this work, we propose GeoSurDepth, a framework that leverages geometry consistency as the primary cue for surround-view depth estimation. Concretely, we utilize vision foundation models as pseudo geometry priors and feature representation enhancement tool to guide the network to maintain surface normal consistency in spatial 3D space and regularize object- and texture-consistent depth estimation in 2D. In addition, we introduce a novel view synthesis pipeline where 2D-3D lifting is achieved with dense depth reconstructed via spatial warping, encouraging additional photometric supervision across temporal and spatial contexts, and compensating for the limitations of target-view image reconstruction. Finally, a newly-proposed adaptive joint motion learning strategy enables the network to adaptively emphasize informative spatial geometry cues for improved motion reasoning. Extensive experiments on KITTI, DDAD and nuScenes demonstrate that GeoSurDepth achieves SoTA performance, validating the effectiveness of our approach. Our framework highlights the importance of exploiting geometry coherence and consistency for robust self-supervised depth estimation.

翻译：精确的环视深度估计为基于激光的传感器提供了有竞争力的替代方案，并且对于自动驾驶中的三维场景理解至关重要。尽管实证研究已提出多种主要关注在光度层面实施跨视图约束的方法，但很少有研究明确利用单目和环视设置中固有的丰富几何结构。在本工作中，我们提出GeoSurDepth，一个以几何一致性作为环视深度估计主要线索的框架。具体而言，我们利用视觉基础模型作为伪几何先验和特征表示增强工具，引导网络在空间三维空间中保持表面法线一致性，并在二维空间中正则化物体与纹理一致的深度估计。此外，我们引入了一种新颖的视图合成流程，其中通过空间扭曲重建的密集深度实现二维到三维的提升，从而鼓励跨时间和空间上下文的光度监督，并弥补目标视图图像重建的局限性。最后，新提出的自适应联合运动学习策略使网络能够自适应地强调信息丰富的空间几何线索，以改进运动推理。在KITTI、DDAD和nuScenes数据集上的大量实验表明，GeoSurDepth实现了最先进的性能，验证了我们方法的有效性。我们的框架凸显了利用几何连贯性与一致性对于实现鲁棒自监督深度估计的重要性。