We propose a simple yet effective method to orient normals for point clouds. Central to our approach is a novel optimization objective function defined from global and local perspectives. Globally, we introduce a signed uncertainty function that distinguishes the inside and outside of the underlying surface. Moreover, benefiting from the statistics of our global term, we present a local orientation term instead of a global one. The optimization problem can be solved by the commonly used numerical optimization solver, such as L-BFGS. The capability and feasibility of our approach are demonstrated over various complex point clouds. We achieve higher practical robustness and normal quality than the state-of-the-art methods.

中文翻译：

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点云的随机法线方向


我们提出了一种简单而有效的方法来定位点云的法线。我们方法的核心是从全局和局部角度定义的新型优化目标函数。在全球范围内，我们引入了一个有符号不确定性函数，用于区分底层表面的内部和外部。此外，受益于我们的全局术语的统计数据，我们提出了一个局部定向术语，而不是全局术语。优化问题可以通过常用的数值优化求解器来解决，例如 L-BFGS。我们方法的能力和可行性在各种复杂的点云上得到了证明。我们实现了比最先进的方法更高的实际稳健性和正常质量。