This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N+ achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85$^\circ$, 8.95$^\circ$, 9.25$^\circ$, and 11.98$^\circ$ on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N+ into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://mias.group/3F2Nplus.

本研究介绍了一种名为3F2N+的新方法，它通过引入一个新的不连续性判别模块（DDM），结合深度曲率最小化和相关系数最大化以及条件随机场，将不连续性判别能力引入表面法线估计器。实验结果表明，3F2N+在各种数据集上实现了优于其他基于几何的表面法线估计器的性能。此外，本研究还展示了将3F2N+应用于机器人感知任务中的有效性。

三滤波器优化: 重新思考深度到法线转换中的不连续性判别