  Despite the growing demand for accurate surface normal estimation models,
existing methods use general-purpose dense prediction models, adopting the same
inductive biases as other tasks. In this paper, we discuss the inductive biases
needed for surface normal estimation and propose to (1) utilize the per-pixel
ray direction and (2) encode the relationship between neighboring surface
normals by learning their relative rotation. The proposed method can generate
crisp - yet, piecewise smooth - predictions for challenging in-the-wild images
of arbitrary resolution and aspect ratio. Compared to a recent ViT-based
state-of-the-art model, our method shows a stronger generalization ability,
despite being trained on an orders of magnitude smaller dataset. The code is
available at https://github.com/baegwangbin/DSINE.
