🟠 Get Average Normal and Center

Function Description

The operator provides multiple calculation methods for computing the average normal and center point of each input point cloud with normals.

Usage Scenarios

  • Object pose estimation: When the overall orientation and center position of a plane or object needs to be determined, for example, providing pose reference for robotic arm grasping.

  • Feature simplification: In complex point cloud processing workflows, simplifying a point cloud patch to a center point and its normal can greatly reduce the complexity of subsequent calculations, facilitating high-level scene analysis or matching.

  • Surface consistency checking: By calculating the average normal of a certain region, you can judge whether the surface orientation of that region is consistent or conforms to expectations.

  • Point cloud matching: In some scenarios, use average normals and center points as coarse alignment matching features.

Input Output

Input

Point Cloud with Normals: One or more point cloud lists with normals.

Output

Average Normal: List of calculated average normals, each normal corresponds to an input point cloud.

Point Cloud Center Point: List of calculated center point coordinates, each center point corresponds to an input point cloud.

Parameter Description

Center Point Type

Parameter Description

Choose the algorithm for calculating point cloud average normals based on project requirements.

  • Direct Mean: Directly sum and average all normal vectors in the input point cloud.

  • Clustering Mean: First cluster all normal vectors, find the cluster with the largest number of normals, then use the average direction of this cluster as the final result.

Parameter Adjustment

  • Direct Mean: This method is fast and straightforward. But if there are noise normals with wrong directions in the point cloud, or it contains multiple faces with opposite directions (such as front and back of a thin sheet), these noises will affect the accuracy of the mean.

  • Clustering Mean: A more robust method that can effectively resist interference from outlier normals, especially suitable for cases with inconsistent normals or multiple main directions.

Average Normal Method

Parameter Description

Choose the method for calculating point cloud center points based on project requirements.

  • Point Cloud Mean: Choose the method for calculating point cloud center points.

  • Point Cloud Median: Calculate the median of X, Y, Z coordinates of all points respectively to form the center point.

  • Min-Max Midpoint: Calculate the midpoint between maximum and minimum values of the point cloud on X, Y, Z axes, i.e., the center of the axis-aligned bounding box (AABB) of the point cloud.

Parameter Adjustment

  • Point Cloud Mean: The most commonly used method, but results are easily affected by noise points (outliers) far from the main point cloud body.

  • Point Cloud Median: Compared to mean, median is much less sensitive to outliers and calculation results are more stable.

  • Min-Max Midpoint: Extremely fast calculation speed, but only considers point cloud boundaries, not internal point distribution.

Clustering Comparison Threshold

Parameter Description

Defines the similarity threshold for judging whether two normal vectors belong to the same cluster during normal clustering. Only takes effect when "Average Normal Method" is set to "Clustering Mean".

Parameter Adjustment

This value controls the strictness of clustering.

  • Decrease this value: Requires normal vectors to be very close to be classified together, clustering becomes stricter and may produce more clusters with fewer quantities.

  • Increase this value: Allows normal vectors with larger directional differences to be classified together, clustering becomes more relaxed. Generally, the default value can meet most requirements.

Parameter Range

[0,1000], Default: 0.1