Density-based Clustering Segmentation (with Normals)
Function: Using the DBSCAN method for clustering point clouds, DBSCAN is a density-based clustering segmentation method. The clustering process starts from a randomly selected seed point and expands until no more points satisfying the density condition can be found. Then, a new seed point is chosen, and the process continues until all points have been traversed
Input Parameters:
| Name | Type | Valid Range | Default Value | Meaning |
|---|---|---|---|---|
Pointcloud |
NormalPoints |
None |
None |
Input point cloud |
Output Parameters:
| Name | Type | Valid Range | Default Value | Meaning |
|---|---|---|---|---|
Split point cloud |
NormalPoints |
None |
None |
Return to split point cloud |
Parameter Settings:
| Name | Type | Valid Range | Default Value | Meaning |
|---|---|---|---|---|
Search radius in |
Float |
[0, 200] |
5 |
The larger the search radius of each seed point, the less clustering |
Density condition |
Integer |
[0, 100000] |
5 |
The minimum number of point cloud points required within the search radius of each seed point, that is, the density condition. The larger the clustering is, the more stringent the clustering is. The smaller the clustering is, the looser the clustering is. The more the clustering is, the more the clustering is. |
Clustering minimum points |
Integer |
[1, 4000000] |
100 |
Minimum number of Point Cloud Points per Cluster |
Clustering max points |
Integer |
[1, 4000000] |
4000000 |
Maximum number of Point Cloud Points per Cluster |
Enable sorting |
Bool |
None |
True |
Sort all output point clouds by number of point clouds from largest to smallest |
Enable node |
Bool |
None |
True |
Turn on node functionality |
Retain all results |
Bool |
[True, False] |
True |
For each input point cloud, whether to output all the split results, if false, the specified number of results will be retained |