Rethinking Inductive Biases for Surface Normal Estimation

Despite the growing demand for accurate surface nor- mal estimation models, existing methods use general- purpose dense prediction models, adopting the same induc- tive 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) en- code 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 magni- tude smaller dataset. The code is available at https://github.com/baegwangbin/DSINE.