pytheia 1.0.3

A performant Structure from Motion library for Python

pyTheia - A Python Structure-from-Motion and Geometric Vision Swiss Knife

pyTheia is based on TheiaSfM. It contains Python bindings for most of the functionalities of TheiaSfM and more.

Documentation: https://urbste.github.io/pyTheiaSfM/ (MkDocs; build locally with pip install -r docs/requirements.txt and mkdocs serve -f docs/mkdocs.yml).

The library is still in active development and the interfaces are not yet all fixed

With pyTheia you have access to a variety of different camera models, structure-from-motion pipelines and geometric vision algorithms.

Differences to the original library TheiaSfM

pyTheia does not aim at being an end-to-end SfM library. For example, building robust feature detection and matching pipelines is usually application and data specific (e.g. image resolution, runtime, pose priors, invariances, ...). This includes image pre- and postprocessing.

pyTheia is rather a "swiss knife" for quickly prototyping SfM related reconstruction applications without sacrificing perfomance. For example SOTA feature detection & matching, place recognition algorithms are based on deep learning, and easily usable from Python. However, using these algorithms from a C++ library is not always straighforward and especially quick testing and prototyping is cumbersome.

Dependency changes

Compared to the original TheiaSfM:

• SuiteSparse: Optional for Ceres; GPL-dependent code was removed in src/math/matrix/sparse_cholesky_llt.cc (cholmod -> Eigen::SimplicialLDLT), which may be slower for very large problems and slightly less stable numerically.
• RapidJSON: No separate dependency; RapidJSON is vendored via cereal headers.
• OpenImageIO / theia/image: Not used. Raster images and EXIF are handled in Python (OpenCV, Pillow, etc.); C++ focuses on geometry, matching structures, and SfM pipelines once correspondences exist.

Changes to the original TheiaSfM library

• Global SfM algorithms:
  • LiGT position solver
  • Lagrange Dual rotation estimator
  • Hybrid rotation estimator
  • Possibility to fix multiple views in Robust_L1L2 solver
  • Nonlinear translation solver can fix multiple view or estimate all remaining views in reconstruction
• Camera models
  • Double Sphere
  • Extended Unified
  • Orthographic
• Bundle adjustment
  • Using a homogeneous representation for scene points
  • Extracting covariance information
  • Possibility to add a depth prior to 3D points
  • Position prior for camera poses (e.g. for GPS or known positions)
• General
  • Added timestamp, position_prior_, position_prior_sqrt_information_ variables to View class Eigen::Matrix3d position_prior_sqrt_information_;
  • Added inverse_depth_, reference_descriptor, reference_bearing_ variables to Track class
  • Added covariance_, depth_prior_, depth_prior_variance_ to Feature class
• Absolute Pose solvers
  • SQPnP
  • UncalibratedPlanarOrthographic Pose

Usage Examples

Full reconstruction example: Global, Hybrid or Incremental SfM using OpenCV feature detection and matching

Have a look at the short example: sfm_pipeline.py. Download the south_building dataset from here. Extract it somewhere and run:

python pytests/sfm_pipeline.py --image_path /path/to/south-building/images/

Creating a camera

The following example show you how to create a camera in pyTheia. You can construct it from a pt.sfm.CameraIntrinsicsPrior() or set all parameters using respective functions from pt.sfm.Camera() class.

import pytheia as pt
prior = pt.sfm.CameraIntrinsicsPrior()
prior.focal_length.value = [1000.]
prior.aspect_ratio.value = [1.]
prior.principal_point.value = [500., 500.]
prior.radial_distortion.value = [0., 0., 0., 0]
prior.tangential_distortion.value = [0., 0.]
prior.skew.value = [0]
prior.camera_intrinsics_model_type = 'PINHOLE' 
#'PINHOLE', 'DOUBLE_SPHERE', 'EXTENDED_UNIFIED', 'FISHEYE', 'FOV', 'DIVISION_UNDISTORTION'
camera = pt.sfm.Camera()
camera.SetFromCameraIntrinsicsPriors(prior)

# the camera object also carries extrinsics information
camera.SetPosition([0,0,-2])
camera.SetOrientationFromAngleAxis([0,0,0.1])

# project with intrinsics image to camera coordinates
camera_intrinsics = camera.CameraIntrinsics()
pt2 = [100.,100.]
pt3 = camera_intrinsics.ImageToCameraCoordinates(pt2)
pt2 = camera_intrinsics.CameraToImageCoordinates(pt3)

# project with camera extrinsics
pt3_h = [1,1,2,1] # homogeneous 3d point
depth, pt2 = camera.ProjectPoint(pt3_h)
# get a ray from camera to 3d point in the world frame
ray = camera.PixelToUnitDepthRay(pt2)
pt3_h_ = ray*depth + camera.GetPosition() # == pt3_h[:3]

Solve for absolute or relative camera pose

pyTheia integrates a lot of performant geometric vision algorithms. Have a look at the tests

import pytheia as pt

# absolute pose
pose = pt.sfm.PoseFromThreePoints(pts2D, pts3D) # Kneip
pose = pt.sfm.FourPointsPoseFocalLengthRadialDistortion(pts2D, pts3D)
pose = pt.sfm.FourPointPoseAndFocalLength(pts2D, pts3D)
pose = pt.sfm.DlsPnp(pts2D, pts3D)
... and more

# relative pose
pose = pt.sfm.NormalizedEightPointFundamentalMatrix(pts2D, pts2D)
pose = pt.sfm.FourPointHomography(pts2D, pts2D)
pose = pt.sfm.FivePointRelativePose(pts2D, pts2D)
pose = pt.sfm.SevenPointFundamentalMatrix(pts2D, pts2D)
... and more

# ransac estimation
params = pt.solvers.RansacParameters()
params.error_thresh = 0.1
params.max_iterations = 100
params.failure_probability = 0.01

# absolute pose ransac
correspondences2D3D = pt.matching.FeatureCorrespondence2D3D(
  pt.sfm.Feature(point1), pt.sfm.Feature(point2))

pnp_type =  pt.sfm.PnPType.DLS #  pt.sfm.PnPType.SQPnP,  pt.sfm.PnPType.KNEIP
success, abs_ori, summary = pt.sfm.EstimateCalibratedAbsolutePose(
  params, pt.sfm.RansacType(0), pnp_type, correspondences2D3D)

success, abs_ori, summary = pt.sfm.EstimateAbsolutePoseWithKnownOrientation(
  params, pt.sfm.RansacType(0), correspondences2D3D)
... and more
# relative pose ransac
correspondences2D2D = pt.matching.FeatureCorrespondence(
            pt.sfm.Feature(point1), pt.sfm.Feature(point2))

success, rel_ori, summary = pt.sfm.EstimateRelativePose(
        params, pt.sfm.RansacType(0), correspondences2D2D)

success, rad_homog, summary = pt.sfm.EstimateRadialHomographyMatrix(
        params, pt.sfm.RansacType(0), correspondences2D2D)  

success, rad_homog, summary = pt.sfm.EstimateFundamentalMatrix(
        params, pt.sfm.RansacType(0), correspondences2D2D)  
... and more

Bundle Adjustment of views or points

import pytheia as pt
recon = pt.sfm.Reconstruction()
# add some views and points
veiw_id = recon.AddView() 
...
track_id = recon.AddTrack()
...
covariance = np.eye(2) * 0.5**2
point = [200,200]
recon.AddObservation(track_id, view_id, pt.sfm.Feature(point, covariance))

# robust BA
opts = pt.sfm.BundleAdjustmentOptions()
opts.robust_loss_width = 1.345
opts.loss_function_type = pt.sfm.LossFunctionType.HUBER

res = BundleAdjustReconstruction(opts, recon)
res = BundleAdjustPartialReconstruction(opts, {view_ids}, {track_ids}, recon)
res = BundleAdjustPartialViewsConstant(opts, {var_view_ids}, {const_view_ids}, recon)

# optimize absolute pose on normalized 2D 3D correspondences
res = pt.sfm.OptimizeAbsolutePoseOnNormFeatures(
  [pt.sfm.FeatureCorrespondence2D3D], R_init, p_init, opts)

# bundle camera adjust pose only
res = BundleAdjustView(recon, opts, view_id)
res = BundleAdjustViewWithCov(recon, view_id)
res = BundleAdjustViewsWithCov(recon, opts, [view_id1,view_id2])

# optimize structure only
res = BundleAdjustTrack(recon, opts, trackid)
res = BundleAdjustTrackWithCov(recon, opts, [view_id1,view_id2])
res = BundleAdjustTracksWithCov(recon, opts, [view_id1,trackid])

# two view optimization
res = BundleAdjustTwoViewsAngular(recon, [pt.sfm.FeatureCorrespondence], pt.sfm.TwoViewInfo())

Export to Nerfstudio and SDFStudio

You can export a pt.sfm.Reconstruction to Nerfstudio or SDFStudio formats directly from Python:

import pytheia as pt
# Nerfstudio (writes transforms.json)
pt.io.WriteNerfStudio("/path/to/images", recon, 16, "/path/to/out/transforms.json")
# SDFStudio (all images must be undistorted)
pt.io.WriteSdfStudio("/path/to/images", recon, (2.0, 6.0), 1.0)

More complete examples are in pyexamples/nerfstudio_export_reconstruction.py and pyexamples/sdfstudio_export_reconstruction.py.

Building

This section describes how to build on Ubuntu locally or on WSL2 (with sudo where noted).

Core dependency: Ceres Solver (non-linear least squares for bundle adjustment and many solvers). A normal Ceres install also pulls in Eigen, glog, and gflags (or your distro equivalents).

• Use a current Ceres 2.x release (see Ceres installation). Older 2.1.x is still fine for CPU-only builds.
• Optional — GPU solvers in Ceres: build Ceres with USE_CUDA=ON for CUDA dense linear algebra. For CUDA sparse (CUDA_SPARSE in Ceres), build Ceres with NVIDIA cuDSS support so the library includes the cuDSS component (details in upstream docs). When you configure pyTheia, CMake detects dense CUDA (compile check) and sparse CUDA (CERES_COMPILED_COMPONENTS); you can override with -DTHEIA_CERES_USE_CUDA / -DTHEIA_CERES_USE_CUDA_SPARSE if needed. Runtime selection of GPU backends is via BundleAdjustmentOptions (dense_linear_algebra_library_type, sparse_linear_algebra_library_type); see the MkDocs chapter Bundle adjustment. Pre-built manylinux wheels typically link a CPU Ceres — use a local build against GPU-enabled Ceres for CUDA backends.

Example: system install (sudo)

sudo apt install cmake build-essential libgflags-dev libgoogle-glog-dev libatlas-base-dev

mkdir LIBS && cd LIBS

# Eigen (example 3.4.x)
git clone https://gitlab.com/libeigen/eigen.git
cd eigen && git checkout 3.4.0
mkdir -p build && cd build && cmake .. && sudo cmake --install .

# Ceres — latest 2.x tag; add -DUSE_CUDA=ON / cuDSS CMake variables per Ceres docs for GPU
cd ../..
git clone https://github.com/ceres-solver/ceres-solver.git
cd ceres-solver && git fetch --tags && git checkout "$(git tag -l '2.*' | sort -V | tail -1)"
mkdir build && cd build
cmake .. -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF -DBUILD_BENCHMARKS=OFF
cmake --build . -j"$(nproc)"
sudo cmake --install .

Local build without sudo

Prefer Ceres EXPORT_BUILD_DIR=ON so find_package(Ceres) can use the build tree. You still need development packages for gflags/glog/atlas (or ask your admin).

mkdir /home/LIBS && cd /home/LIBS

git clone https://gitlab.com/libeigen/eigen.git
cd eigen && git checkout 3.4.0
mkdir -p build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/home/LIBS/eigen/build && cmake --build . -j"$(nproc)" && cmake --install .

cd /home/LIBS
git clone https://github.com/ceres-solver/ceres-solver.git
cd ceres-solver && git fetch --tags && git checkout "$(git tag -l '2.*' | sort -V | tail -1)"
mkdir build && cd build
cmake .. -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF -DBUILD_BENCHMARKS=OFF -DEXPORT_BUILD_DIR=ON
cmake --build . -j"$(nproc)"

cd /path/to/pyTheiaSfM && mkdir build && cd build
cmake -DEigen3_DIR=/home/LIBS/eigen/build/share/eigen3/cmake/ -DCeres_DIR=/home/LIBS/ceres-solver/build ../
cmake --build . -j"$(nproc)"

Full narrative (system deps, CUDA notes, docs build): docs/content/building.md (also rendered as Building on the project docs site).

How to build Python wheels

Local build with sudo installed ceres-solver and Eigen

Tested on Ubuntu. In your Python >= 3.6 environment of choice run:

sh build_and_install.sh

If you have problems like /lib/libstdc++.so.6: version `GLIBCXX_3.4.30' not found on Ubuntu 22.04 in an Anaconda environment try:

conda install -c conda-forge libstdcxx-ng

Another solution is to check the GLIBCXX versions. If the version that the library requires is installed, then we can create a symbolic link into the conda environment.

strings /usr/lib/x86_64-linux-gnu/libstdc++.so.6 | grep GLIBCXX
# if the GLIBCXX version is available then do:
ln -sf /usr/lib/x86_64-linux-gnu/libstdc++.so.6 ${CONDA_PREFIX}/lib/libstdc++.so.6

With Docker

The docker build will actually build manylinux wheels for Linux (Python 3.6-3.12). There are two ways to do that. One will clutter the source directory, but you will have the wheel file directly available (./wheelhouse/). Another drawback of this approach is that the files will have been created with docker sudo rights and are diffcult to delete:

# e.g. for python 3.9
docker run --rm -e PYTHON_VERSION="cp39-cp39" -v `pwd`:/home urbste/pytheia_base:1.4.0 /home/pypackage/build-wheel-linux.sh

The other one is cleaner but you will have to copy the wheels out of the docker container afterwards:

docker build -t pytheia:1.0 .
docker run -it pytheia:1.0

Then all the wheels will be inside the container in the folder /home/wheelhouse. Open a second terminal and run

docker ps # this will give you a list of running containers to find the correct CONTAINER_ID
docker cp CONTAINER_ID:/home/wheelhouse /path/to/result/folder/pytheia_wheels

Typing and editor stubs

To get full function/argument lists and IntelliSense in editors for the native extension:

• Generate stubs locally (requires pybind11-stubgen):

  pip install pybind11-stubgen
  dev/generate_stubs.sh
  

  This writes .pyi files to typings/pytheia. VS Code/Pylance will pick them up via pyrightconfig.json.

• When building wheels via setup.py, stubs are generated automatically by default. To skip:

  GENERATE_STUBS=0 python setup.py bdist_wheel --plat-name=...
  

• The package ships a PEP 561 marker (py.typed) so downstream type checkers can consume the bundled stubs.