  Surface normal holds significant importance in visual environmental
perception, serving as a source of rich geometric information. However, the
state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from
an unsatisfactory trade-off between efficiency and accuracy. To resolve this
dilemma, this paper first presents a superfast depth-to-normal translator
(D2NT), which can directly translate depth images into surface normal maps
without calculating 3D coordinates. We then propose a discontinuity-aware
gradient (DAG) filter, which adaptively generates gradient convolution kernels
to improve depth gradient estimation. Finally, we propose a surface normal
refinement module that can easily be integrated into any depth-to-normal SNEs,
substantially improving the surface normal estimation accuracy. Our proposed
algorithm demonstrates the best accuracy among all other existing real-time
SNEs and achieves the SoTA trade-off between efficiency and accuracy.
