Gaussian Smoothing（高斯平滑）

高斯滤波器会基于3D距离对较小局部区域内的点进行平滑处理。这可用于抑制稀疏噪声、校正异常值以及将点与网格对齐。 Sigma 参数决定了过滤器的平滑程度。Sigma值越高，平滑越积极。请注意，在许多情况下，高斯滤波器可以修正离群值，但较高的Sigma值也会平滑边缘。

高斯平滑过滤器在每次3D捕获会增加以下处理时间：

什么时候使用高斯平滑？

Gaussian smoothing is useful when you want to reduce absolute noise in a point cloud before further processing. Lower noise levels can improve the robustness and stability of certain vision algorithms, particularly in cases where noise causes false detections or unstable results. By reducing high frequency variations, Gaussian smoothing makes the data more consistent overall. While the relative variation between regions remains the same, the absolute noise level is reduced, which can help algorithms produce more stable outputs.

Gaussian smoothing is especially beneficial when evaluating large scale geometric properties, such as plane flatness. In these cases, suppressing outliers and small local variations can prevent the algorithm from producing overly pessimistic measurements. However, smoothing also reduces fine detail. If an algorithm relies on sharp edges or small features, excessive smoothing may reduce detection accuracy. Therefore, the amount of smoothing should be chosen carefully based on the application.

版本历史

SDK	变更
2.9.0	减少过滤器处理时间并提高边缘保留能力。
1.3.0	添加了高斯过滤器API。