This release is a pre-release and may not be stable for production use.
vernier
Geometric calibration, fast. Multi-camera intrinsics, multi-sensor extrinsics, and bundle adjustment through a clean Python API on top of a Ceres Solver / Sophus core.
pip install pyvernier
import numpy as np
import vernier
K = np.array([[1400, 0, 960], [0, 1400, 540], [0, 0, 1]], dtype=np.float64)
distortion = np.zeros(5)
estimator = vernier.Estimator(loss="huber", loss_scale=1.0)
camera = estimator.add_camera("brown_conrady", K, distortion, fixed=["k3"])
frame = estimator.add_frame(vernier.SE3(), vernier.Submanifold.SE3)
for point_3d, pixel_2d in observations:
estimator.add_observation(camera, frame, point_3d, pixel_2d)
result = estimator.solve(max_iterations=200)
# SolveResult(converged=True, rms=0.31, iterations=8, time=0.076)
K_opt = estimator.get_camera_matrix(camera)
dist_opt = estimator.get_dist_coefficients(camera)
pose_opt = estimator.get_frame_pose(frame)
Features
Projection Models
| Model | Distortion vector | Use case |
|---|---|---|
BrownConrady |
[k1, k2, p1, p2, k3] |
Standard rectilinear cameras (OpenCV default) |
KannalaBrandt |
[k1, k2, k3, k4] |
Fisheye / wide-angle |
Division |
[k1, k2] |
Lightweight radial distortion |
Rational |
[k1..k6, p1, p2] |
Complex optics |
# String API
camera = estimator.add_camera("kannala_brandt", K, dist)
# Class API
camera = estimator.add_camera(vernier.KannalaBrandt, K, dist)
Per-Parameter Fixing
Hold any subset of intrinsic parameters constant during optimization:
# Fix k3 by name
camera = estimator.add_camera("brown_conrady", K, dist, fixed=["k3"])
# Fix by enum
camera = estimator.add_camera(
vernier.BrownConrady, K, dist,
fixed=[vernier.BrownConrady.Param.K3],
)
# Optimize distortion only
camera = estimator.add_camera(
"brown_conrady", K, dist,
fixed=["fx", "fy", "cx", "cy"],
)
Submanifold Pose Constraints
Choose which degrees of freedom of an SE(3) frame are optimized:
# Full 6-DOF (default)
frame = estimator.add_frame(pose, vernier.Submanifold.SE3)
# Translation only
frame = estimator.add_frame(pose, vernier.Submanifold.TRANSLATION)
# Rotation only
frame = estimator.add_frame(pose, vernier.Submanifold.ROTATION)
# Arbitrary axis combination
frame = estimator.add_frame(
pose,
vernier.Submanifold.X | vernier.Submanifold.Y | vernier.Submanifold.WZ,
)
# Held fixed
frame = estimator.add_frame(pose, vernier.Submanifold.NONE)
Robust Loss Functions
Per-observation loss for outlier handling. Default is Huber (1.0 px) — the standard in bundle adjustment.
| Loss | Behavior | When to use |
|---|---|---|
"none" |
Pure L2 | Clean synthetic data |
"huber" |
L2 below scale, L1 above | Default — calibration with mild outliers |
"cauchy" |
Aggressive downweighting | Noisy real-world data |
"soft_l1" |
Smooth L1 approximation | Dense correspondences |
# Estimator-wide default
estimator = vernier.Estimator(loss="cauchy", loss_scale=0.5)
# Per-observation override
estimator.add_observation(camera, frame, p3d, p2d, loss="cauchy", loss_scale=0.5)
Architecture
src/vernier/
├── __init__.py Public re-exports + version
├── _core.py Lie groups, projection models, enums, results
└── estimator.py Estimator — Pythonic problem-construction API
The optimization core wraps Ceres Solver for
non-linear least squares with autodiff and Sophus
for SE(3) / SO(3) manifold operations. Projection models are stateless C++
trait structs dispatched via std::variant + std::visit so Ceres gets
exact Jacobian block sizes per camera.
Documentation
Full documentation will be available at vistralis.org/vernier.
Development
pip install -e ".[dev]"
ruff check .
ruff format --check .
pytest
License
Apache-2.0 — see LICENSE for details.
Copyright (c) 2026 Vistralis Labs.
1 maintainer
Metadata
1 maintainer
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