File Serge3dxmeasuringcontestandprincipa Free May 2026

# Compute PCA (Principal Component Analysis) centroid = vertices.mean(axis=0) centered = vertices - centroid cov = np.cov(centered.T) eigenvalues, eigenvectors = np.linalg.eig(cov)

| Source | What You Get | PCA/Principal Ready? | |--------|--------------|----------------------| | | Medical STL files for contest measuring | Yes, use above script | | Thingiverse "Calibration" | Calibration cubes, torture tests | Yes | | GrabCAD Challenge | Past competition parts + measurement answers | Yes | | AIM@SHAPE | Standard 3D benchmark models (Stanford Bunny, Dragon) | Yes | file serge3dxmeasuringcontestandprincipa free

# pca_align.py - Free & Open Source import numpy as np import trimesh def align_to_principal_axes(mesh_path, output_path): # Load mesh mesh = trimesh.load(mesh_path) vertices = mesh.vertices # Compute PCA (Principal Component Analysis) centroid =