There are several applications that come with Theia right out of the box. These applications are useful on their own, and also help provide context for how Theia can be used for your own applications. Only minimal documentation is provided here, but a full description of command line arguments and more can be found within each application file.
The general way to run each of the applications (after building Theia) is by running the executables and supplying the required command line flags. The command line flags may be determined by running the executable followed by
--helpshort. This will supply a list of required command line flags with a short description of their meaning, as well as the default parameters. When many flags are required to run a program, it is advisable to put all the flags into a single .txt file and supply them as a “flagfile” such as:
In order to view the logging that Theia provides (which can be extremely useful!) you will have to add the command line flag
Extract any type of feature that is implemented in Theia (e.g., SIFT) and write them to disk.
./bin/extract_features --input_images=/path/to/images/*.jpg --features_output_director=/path/to/output --num_threads=4 --descriptor=SIFT --logtostderr
This application will build a 3D reconstruction from a set of images or a set of image matches. Detailed documentation for the structure-from-motion pipeline can be found at Structure from Motion (SfM). Many parameters can be set at runtime (too many to list here).
We provide an example of the possible command line flags for
build_reconstructions in applications/build_reconstruction_flags.txt. We highly recommend that you copy this file then adjust the parameters for your own dataset and settings.
Once you have your flags file, you may create a 3D reconstruction by executing the command:
The reconstruction parameters may need to be tuned a bit for the individual datasets.
If images are supplied as input, then features are extracted and matched between images before the reconstruction process begins. It is advised that you save these matches (by specifying a –output_matches_file=/path/to/output.matches) so that the reconstruction process may be restarted directly from the two-view geometry. This allows you to tune the reconstruction parameters without having to wait for image matching which is typically the slowest part of structure-from-motion. Alternatively, you could first generate the two view geometry and save the information using the program below.
The 1DSfM dataset is an excellent dataset for SfM reconstructions from internet photo collections and is a benchmark dataset for medium to large-scale SfM reconstructions. Since Theia is aimed to make research and experimentation simple, we have provided an interface to directly utilize the 1DSfM datasets without having to worry about processing the data yourself.
We provide an example of the possible command line flags for
build_1dsfm_reconstructions in applications/build_1dsfm_reconstruction_flags.txt. We highly recommend that you copy this file then adjust the parameters for your own dataset and settings.
By running the following command, you can utilize Theia’s reconstruction pipeline directly on the 1DSfM dataset:
After computing SfM reconstructions, it can be useful to compare them. For example, two reconstructions may be created with different parameters then compared to determine how the various parameters affect reconstruction quality. Running this program will output statistics such as rotation different, positions difference, and the difference between camera intrinsic parameters.
./bin/compare_reconstructions --reference_reconstruction=ground_truth_reconstruction --reconstruction_to_align=your_reconstruction --logtostderr
Note that reference_reconstruction is considered the “ground truth” reconstruction for this application. The reconstruction in reconstruction_to_align is aligned to reference_reconstruction with a similarity transformation (aligning the cameras with the same name in both reconstructions) then the errors are measured.
For the 1DSfM dataset, you can use the
to determine the ground truth errors. First, use the
application to convert the ground truth Bundler files that come with the 1DSfM
dataset of interest. Then compare the reconstruction computed with Theia to the
ground truth reconstruction using the command line above. Since the ground truth
1DSfM bundler files are roughly metric-scale, the positions errors will be
approximately in meters.
Similarly, for the Strecha Dataset, you can first create a ground truth
reconstruction with the
program. Then use this as the ground truth reconstruction for
compare_reconstructions. Similar to the 1DSfM datasets, the ground truth
Strecha reconstructions are metric-scale and so are the position errors.
Compute Two View Geometry¶
Computes the two view matches and geometry between image pairs. This program follows many of the same parameters as the Build Reconstructions program, but is useful for generating two view geometries prior to building a reconstruction. Feature matching is performed between images then geometric verification is performed to determine which feature matches are inliers. Only image pairs that have sufficiently many geometrically-verified matches are considered valid.
Compute Reconstruction Statistics¶
Computes some basic information about reconstructions such as reprojection error, number of cameras, 3D points, and the average number of observations per 3D point.
./bin/compute_reconstruction_statistics --reconstruction=my_reconstruction --logtostderr
Compute Matching Relative Pose Errors¶
Two-view matches are the input to SfM, so the quality of the matches is important to the final quality of the SfM reconstruction. To evaluate the accuracy of various matching strategies (e.g., brute force vs cascade hashing, or whether to perform two-view bundle adjustment), you can compare the input two-view matches and geometry to the final reconstruction.
./bin/compute_matching_relative_pose_errors --matches=matches_file --reconstruction=ground_truth_reconstruction --logtostderr
A very basic OpenGL point cloud viewer.
I am not an OpenGL expert so I welcome and encourage any improvements to the reconstruction viewer.
The reconstruction file can be generated using the
The viewer currently displays all points with black, though in the future we may record pixel color data. The cameras are displayed according to their intrinsic parameters, so the size and shape of the camera wireframes is indicative of the principal points, image width and height, and the focal length.
The controls are:
LEFT MOUSE CLICK + DRAG: Moves the position of the scene relative to the current viewpoint i.e., dragging left will move the scene to the left, etc.
RIGHT MOUSE CLICK + DRAG: Rotates the camera around the scene.
MOUSE SCROLL UP or z: Zooms the camera into the scene.
MOUSE SCROLL DOWN or SHIFT + z: Zooms the camera away from the scene.
f: Decreases the size of the cameras relative to the scene.
SHIFT + f: Increases the size of the cameras relative to the scene.
p: Decrease the size of the points in the point cloud (
NOTE: there is a minimum size).
P: Increase the size of the points in the point cloud.
c: Toggle to choose whether to display or not display camera wireframes.
t: Increase the minimum number of views that must observe a 3D point in order for it to be displayed. By default, each 3D point must be observed by 2 views in order to be displayed. Increasing this value will often result in a more clear reconstruction.
T: Decrease the minimum number of views that must observe a 3D point in order for it to be displayed.
Create Calibration File From EXIF¶
Creates a calibration file from the EXIF information that can be extracted from an image set.
./bin/create_calibration_file_from_exif --images=/path/to/images/*.jpg --output_calibration_file=/path/to/output/calibration.txt
Converting to Bundler and NVM formats¶
We provide conversion to to and from Bundler and NVM files. Take a look at convert_bundle_file.cc, convert_nvm_file.cc, convert_theia_reconstruction_to_bundler_file.cc, and export_to_nvm_file.cc.
Additionally, we provide at tool to convert the Theia reconstruction to the PMVS format in the export_reconstruction_to_pmvs.cc.
Calibrate Camera Intrinsics¶
Often it is difficult to obtain good camera calibration, and personally I have never found OpenCV’s calibration to work as reliably as I would like (particularly for fisheye lenses). I have written a simple calibration tool that takes in images and runs incremental SfM while optimizing camera intrinsics. Then, the optimized intrinsics are used as the priors for a fresh restart of incremental SfM and the process is repeated for several iterations. The final calibration is printed as upon termination.
The calibration toolkit has worked well for me if the input is a well textured scene. You may supply which camera model you would like to use, and many other parameters that may be found in the
calibrate_camera_intrinsics_flags.txt file. Please use this flags file as your starting point when using the calibration module.