We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.

翻译：我们提出了一种在几何上下文信息引导下从图像中学习深度与表面法线等几何信息的新方法。现有方法在可靠捕获几何上下文方面的局限性阻碍了其精确施加不同几何属性间一致性的能力，从而成为几何估计质量的瓶颈。为此，我们提出自适应表面法线（ASN）约束——一种简洁高效的方法。本方法通过提取编码输入图像几何变化的上下文信息，将深度估计与几何约束相关联。通过从随机采样候选点中动态确定可靠的局部几何结构，我们构建了表面法线约束，并利用几何上下文评估这些候选点的有效性。此外，我们的法线估计方法利用几何上下文优先处理几何变化显著的区域，使得预测的法线能精确捕获复杂精细的几何细节。通过几何上下文的整合，本方法将深度与表面法线估计统一于连贯框架中，从而能够从图像生成高质量三维几何。通过在多样化室内外数据集上的全面评估与对比，我们验证了本方法相较于现有最优方法的优越性，展示了其高效性与鲁棒性。