hausdorff-stats 0.1.0

Multi-stat Hausdorff distance for 3D mesh surfaces (min, max, mean, percentile)

hausdorff-stats

Multi-stat bidirectional Hausdorff distance for 3D mesh surfaces.

Returns min, max, mean, and 95th-percentile distances in both directions — not just the single worst-case number.

Installation

pip install hausdorff-stats

For point-to-cell distance (projects onto triangle surfaces via VTK):

pip install hausdorff-stats[vtk]

Quick start

Point-to-point (numpy arrays)

import numpy as np
from hausdorff_stats import hausdorff_metrics

# Two point clouds of shape (N, 3)
a = np.random.rand(500, 3)
b = np.random.rand(600, 3) + 0.1

result = hausdorff_metrics(a, b)

result.hausdorff      # bidirectional Hausdorff distance
result.mean_a_to_b    # mean distance from A to B
result.p95_b_to_a     # 95th percentile from B to A
result.summary()      # dict of all stats (handy for pandas / JSON)

Point-to-cell (VTP mesh files)

from hausdorff_stats import hausdorff_metrics

result = hausdorff_metrics(
    "surface_a.vtp",
    "surface_b.vtp",
    method="point_to_cell",
)

This projects each point onto the nearest triangle cell on the opposite surface, giving more accurate distances for tessellated geometry.

Available statistics

hausdorff_metrics returns a HausdorffResult with:

Property	Description
hausdorff	Bidirectional Hausdorff distance (max of both directed maxima)
min_a_to_b / min_b_to_a	Minimum directed distance
max_a_to_b / max_b_to_a	Maximum directed distance
mean_a_to_b / mean_b_to_a	Mean directed distance
p95_a_to_b / p95_b_to_a	95th percentile directed distance
percentile(q)	Arbitrary percentile for both directions
summary()	Flat dict of all stats

Raw per-point distance arrays are also accessible as distances_a_to_b and distances_b_to_a.

License

MIT