  We present a novel method for single image depth estimation using surface
normal constraints. Existing depth estimation methods either suffer from the
lack of geometric constraints, or are limited to the difficulty of reliably
capturing geometric context, which leads to a bottleneck of depth estimation
quality. We therefore introduce a simple yet effective method, named Adaptive
Surface Normal (ASN) constraint, to effectively correlate the depth estimation
with geometric consistency. Our key idea is to adaptively determine the
reliable local geometry from a set of randomly sampled candidates to derive
surface normal constraint, for which we measure the consistency of the
geometric contextual features. As a result, our method can faithfully
reconstruct the 3D geometry and is robust to local shape variations, such as
boundaries, sharp corners and noises. We conduct extensive evaluations and
comparisons using public datasets. The experimental results demonstrate our
method outperforms the state-of-the-art methods and has superior efficiency and
robustness.
