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：我们提出了一种新颖的方法，在融合几何上下文的同时从图像中学习深度和表面法向等几何信息。现有方法难以可靠地捕获几何上下文，这阻碍了它们精确保持不同几何属性之间一致性的能力，从而成为几何估计质量的瓶颈。因此，我们提出了自适应表面法向（ASN）约束，这是一种简单而高效的方法。我们的方法提取编码输入图像中几何变化的几何上下文，并将深度估计与几何约束相关联。通过从随机采样的候选点中动态确定可靠的局部几何结构，我们建立了表面法向约束，并利用几何上下文评估这些候选点的有效性。此外，我们的法向估计利用几何上下文优先处理几何变化显著的区域，使预测的法向能够精确捕获复杂且细微的几何信息。通过整合几何上下文，我们的方法将深度和表面法向估计统一在一个连贯的框架中，从而能够从图像生成高质量的三维几何结构。通过在多种室内外数据集上的广泛评估与对比，我们验证了该方法相较于现有最优技术的优越性，展示了其高效性和鲁棒性。