biocpd 1.2.0

Coherent Point Drift variants (rigid, affine, deformable, PCA/SSM) in NumPy/SciPy

biocpd

Coherent Point Drift (CPD) registration in pure NumPy/SciPy with fast variants:

• Rigid and Affine CPD
• Deformable CPD with low-rank (randomized SVD) and k-d tree accelerated E-step
• Constrained Deformable CPD with correspondence priors
• Atlas/SSM-based CPD (AtlasRegistration) optimized in coefficient space

Why biocpd?

• Fast: sparse k-NN E-step, low-rank kernels, and efficient linear solvers
• Flexible: rigid, affine, unconstrained and constrained deformable, and SSM/atlas-based
• Simple: pure NumPy/SciPy implementation; easy to read and extend

Install

pip install -r requirements.txt
# optional (recommended for building)
pip install build wheel

Build wheel

# From repository root
python -m build
# or legacy
python setup.py sdist bdist_wheel

Quickstart

import numpy as np
from biocpd import RigidRegistration, AffineRegistration, DeformableRegistration, ConstrainedDeformableRegistration, AtlasRegistration

rng = np.random.default_rng(0)
X = rng.normal(size=(200, 3))           # target
Y = X + 0.05 * rng.normal(size=(200,3)) # source (noisy)

# Rigid CPD
rig = RigidRegistration(X=X, Y=Y, max_iterations=50, use_kdtree=True, k=10)
TY_rigid, (s, R, t) = rig.register()

# Affine CPD
aff = AffineRegistration(X=X, Y=Y, max_iterations=50, use_kdtree=True, k=10)
TY_affine, (B, t) = aff.register()

# Deformable CPD (low-rank + k-d tree)
defm = DeformableRegistration(X=X, Y=Y, alpha=2.0, beta=2.0, low_rank=True, num_eig=80,
                              use_kdtree=True, k=10, radius_mode=False, w=0.05,
                              max_iterations=50)
TY_def, params = defm.register()

# Constrained Deformable CPD
ids = np.arange(10)
con = ConstrainedDeformableRegistration(X=X, Y=Y, alpha=2.0, beta=2.0, low_rank=True, num_eig=80,
                                        use_kdtree=True, k=10, e_alpha=1e-4,
                                        source_id=ids, target_id=ids,
                                        max_iterations=50)
TY_con, params_con = con.register()

# Atlas / Statistical Shape Model CPD
M, D, K = 200, 3, 12
mean_shape = rng.normal(size=(M, D))
U = rng.normal(size=(M*D, K))
L = np.abs(rng.normal(size=(K,))) + 1e-1
atl = AtlasRegistration(X=X, Y=mean_shape, mean_shape=mean_shape,
                        U=U, eigenvalues=L, lambda_reg=0.1,
                        normalize=True, use_kdtree=True, k=10, radius_mode=False,
                        optimize_similarity=True, with_scale=True, w=0.02,
                        max_iterations=50)
TY_atl, params_atl = atl.register()

Key options

• use_kdtree, k: enable sparse E-step for speed on large data
• low_rank, num_eig (deformable): low-rank kernel for fast M-step
• radius_mode: optional radius gating in sparse E-step (off by default)
• w: outlier weight (0 ≤ w < 1) in GMM
• dtype (deformable, constrained deformable, atlas): defaults to np.float32; set dtype=np.float64 when you need the extra precision
• normalize (atlas): improves stability across scales
• mean_shape (atlas): with normalize=True, pass mean_shape as (M, D)

Acknowledgements

• This work builds on the excellent original CPD implementation by Siavash Khallaghi and Anthony Gatti (pycpd, MIT-licensed) and the CPD method by Myronenko and Song.
• Repository for pycpd: https://github.com/siavashk/pycpd

Citation

If you use this package in academic work, please cite CPD:

• Myronenko, A. and Song, X., "Point Set Registration: Coherent Point Drift," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.

License

MIT