[3D CV] DBSCAN clustering

DBSCAN clustering open3d code

DBSCAN(Density-based spatial clustering of applications with noise)

Given a point cloud from e.g. a depth sensor we want to group local point cloud clusters together. For this purpose, we can use clustering algorithms. Open3D implements DBSCAN [Ester1996] that is a density based clustering algorithm.

The algorithm is implemented in cluster_dbscan and requires two parameters:

• eps defines the distance to neighbors in a cluster
• min_points defines the minimum number of points required to form a cluster.
• The function returns labels, where the label -1 indicates noise.

pcd = o3d.io.read_point_cloud("../../test_data/fragment.ply")

with o3d.utility.VerbosityContextManager(
        o3d.utility.VerbosityLevel.Debug) as cm:
    labels = np.array(
        pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))

max_label = labels.max()
print(f"point cloud has {max_label + 1} clusters")
colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
colors[labels < 0] = 0
pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
o3d.visualization.draw_geometries([pcd],
                                  zoom=0.455,
                                  front=[-0.4999, -0.1659, -0.8499],
                                  lookat=[2.1813, 2.0619, 2.0999],
                                  up=[0.1204, -0.9852, 0.1215])

[Open3D DEBUG] Precompute Neighbours
[Open3D DEBUG] Done Precompute Neighbours
[Open3D DEBUG] Compute Clusters
[Open3D DEBUG] Done Compute Clusters: 10
point cloud has 10 clusters