  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.
