Point Cloud Normal Estimation

This example demonstrates how to estimate normals from a point cloud using COMPAS CGAL.

Key Features:

• Loading point clouds from PLY files
• Point cloud density reduction
• Normal estimation using k-nearest neighbors
• Visualization of point normals as scaled lines

from pathlib import Path

from compas.geometry import Line
from compas.geometry import Pointcloud
from compas_viewer import Viewer
from compas_viewer.config import Config

from compas_cgal.reconstruction import pointset_normal_estimation
from compas_cgal.reconstruction import pointset_reduction

# ==============================================================================
# Input geometry
# ==============================================================================

FILE = Path(__file__).parent.parent.parent / "data" / "forked_branch_1.ply"

cloud = Pointcloud.from_ply(FILE)
reduced_cloud = Pointcloud(pointset_reduction(cloud, 10))
points, vectors = pointset_normal_estimation(reduced_cloud, 16, True)

# ==============================================================================
# Compute
# ==============================================================================

lines = []
line_scale = 10

for p, v in zip(points, vectors):
    line = Line(
        [p[0], p[1], p[2]],
        [
            p[0] + v[0] * line_scale,
            p[1] + v[1] * line_scale,
            p[2] + v[2] * line_scale,
        ],
    )
    lines.append(line)

# ==============================================================================
# Visualize
# ==============================================================================

config = Config()
config.unit = "mm"

viewer = Viewer(config=config)

group = viewer.scene.add_group("pointset")
group.add_from_list(lines)

viewer.show()