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.

翻译：表面法向在视觉环境感知中具有重要意义，是丰富几何信息的来源。然而，现有最先进的表面法向估计器通常面临效率与精度之间的不理想权衡。为解决这一困境，本文首先提出一种超快深度到法向翻译器（D2NT），该翻译器可直接将深度图像转换为表面法向图，无需计算三维坐标。接着，我们提出一种不连续性感知梯度滤波器，该滤波器自适应生成梯度卷积核以改进深度梯度估计。最后，我们提出一个表面法向细化模块，该模块可轻松集成到任何基于深度到法向的表面法向估计器中，显著提升表面法向估计精度。所提算法在所有现有实时表面法向估计器中展现出最佳精度，并实现了效率与精度的最优权衡。