Accurate surround-view depth estimation provides a competitive alternative to laser-based sensors and is essential for 3D scene understanding in autonomous driving. While prior studies have proposed various approaches that primarily focus on enforcing cross-view constraints at the 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 foundation models as a pseudo geometry prior 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, spatial, and spatial-temporal contexts, and compensating for the limitations of single-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 DDAD and nuScenes demonstrate that GeoSurDepth achieves state-of-the-art performance, validating the effectiveness of our approach. Our framework highlights the importance of exploiting geometry coherence and consistency for robust self-supervised multi-view 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一种具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。尽管先前的研究提出了多种方法，主要侧重于在光度层面加强跨视图约束，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，我们提出了GeoSurDepth，一个利用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和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稠密深度实现二维到三维的提升，从而鼓励跨时间、空间和时空上下文的额外光度监督，并弥补单视图图像重建的局限性。最后，一种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DDAD和nuScenes数据集上进行的大量实验表明，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相干性和一致性对于鲁棒的自监督多视图深度估计的重要性。