Main page → Application Settings → Application Settings: Live Reconstruction
Reconstruction settings configured under the Application Settings apply only to the real-time reconstruction in Live mode. Parameters for post-processing reconstruction pipelines can be modified from corresponding Take properties under the Properties pane.
The real-time reconstruction settings can be accessed in the Reconstruction tab under the Application Settings pane.
Reconstruction in motion capture is a process of deriving 3D points from 2D coordinate information obtained from captured images, and the Point Cloud is the core engine that runs the reconstruction process. The reconstruction settings define the parameters of the point cloud engine, and they can be modified to optimize the acquisition of 3D data points.
For more information on how to utilize the reconstruction settings, visit Reconstruction and 2D Mode page.
The Point Cloud reconstruction engine converts two-dimensional point from camera images into coordinates in a three-dimensional space through triangulation. All cameras should be calibrated for the engine to function properly (see Calibration). The triangulation of a marker occurs when a minimum of 2 rays intersect. Rays are generated from the objects present on a camera image and they resolve into a 3D point when the conditions defined by the reconstructions settings are met. These rays can be seen from the Perspective View pane when the tracked rays and untracked rays are enabled from the visibility settings.
Due to inherent errors in marker tracking, rays generally do not converge perfectly on a single point in 3D space, so a tolerance value is defined. This tolerance, called the residual, represents one of the reconstruction constraints. If a ray could be defined as an infinite series of points aligned in a straight line, two or more rays that have points within the defined residual range (in mm) will form a marker.
Default: 10.00 mm
The residual value sets the maximum allowable offset distance (in mm) between rays contributing to a single 3D point.
When the residual value is set too high, unassociated marker rays may contribute to marker reconstruction, and non-existing ghost markers may be reconstructed. When this value is set too low, the contributing rays within a marker could reconstruct multiple markers where there should only be one.
Depending on the size of markers used, the contributing rays will converge with a varying tolerable offset. If you are working with smaller markers, set the residual value lower. If you're working with larger markers, set this value higher because the centroid rays will not converge as precisely as the smaller markers. A starting point is to set the residual value to the diameter of the smallest marker and go down from there until you start seeing ghost markers. For example, when 3 mm and 14 mm markers are captured in a same volume, set the residual value to less than 3 mm. The ghost markers can appear on larger markers if this value is set too low.
The residual can also be viewed as the minimum distance between two markers before they begin to merge. If two markers have a separation distance smaller than the defined residual (in mm), the contributing rays for each marker will be merged and only one marker will be reconstructed, which is undesirable. Remember that for a 3D point to be reconstructed, it needs to have at least two rays contributing to a marker depending on the Minimum Rays setting.
If calibration quality is not very good, you may need to set this value higher for increased tolerance. This will work only if your markers are further apart in the 2D views throughout the given marker motion. This is because there is more errors in the system. However, for best results, you should always work with a calibration with minimal error (See Calibration).
Default: None — the calibration solver will set a suggested distance based on the wanding results, but this can still be adjusted by the user after calibration.
This sets the maximum distance, in meters, a marker can be from the camera to be considered for 3D reconstruction. In very large volumes with high resolution cameras, this value can be increased for a longer tracking range or to allow contributions from more cameras in the setup. This setting can also be reduced to filter out longer rays from reconstruction. Longer rays generally produce less accurate data than shorter rays.
When capturing in a large-size volume with a medium-size – 20 ~ 50 cameras – camera system, this setting can be adjusted for better tracking results. Tracking rays from cameras at the far end of the volume may be inaccurate for tracking markers on the opposite end of the volume, and the unstable rays may contribute to ghost marker reconstructions. In this case, lower the maximum ray length to restrict reconstruction contributions from cameras tracking at long distances. For captures vulnerable to frequent marker occlusions, adjusting this constraint is not recommended since more camera coverage is needed for preventing the occlusions. Note that lowering this setting can take a toll on performance at higher camera counts and marker counts because the solver has to perform numerous calculations per second to decide which rays are good.
Default: 0.2 m
This sets the minimum distance, in meters, between a marker and a camera for the camera to contribute to the reconstruction of the marker. When ghost markers appear close to the camera lens, increase this setting to restrict the unwanted reconstructions in the vicinity. But for close-range tracking applications, this setting must be set low.
Default: 2 rays
This sets the required minimum number of cameras that must see a marker for it to be reconstructed.
For a marker to be reconstructed, at least two or more cameras need to see the marker. The minimum rays setting defines the required number of cameras that must see a marker for it to be reconstructed. If you have 4 cameras and set this to 4, all cameras must see the marker; otherwise, the marker will not be reconstructed and the contributing rays will become the untracked rays.
When more rays are contributing to a marker, more accurate reconstruction can be achieved, but generally, you don't need all cameras in a setup to see a marker. If you have a lot of cameras capturing a marker, you can safely increase this setting to prevent false reconstructions which may come from 2 or 3 rays that happen to connect within the residual range. However, be careful when increasing this setting because a high number of minimum rays requirement may decrease the effective capture volume and increase the frequency of marker occlusions during capture.
Configures Motive for tracking either the passive markers, the synchronized active markers, or both. See Active Marker Tracking for more information.
This setting is available only if marker labeling mode is set to one of the active marker tracking modes. This setting sets the complexity of the active illumination patterns. When tracking a high number of rigid body, this may need to be increased to allow for more combinations of the illumination patterns on each marker. When this value is set too low, the active labeling will not work properly.
Enable or disable continuous calibration. When enabled, Motive will continuously monitor the calibration quality and update it as necessary. For more information, refer to the Continuous Calibration page.
This property was called Ray Ranking in older versions
This setting enables the Ray Ranking, which calculates quality of each ray to potentially improve the reconstruction. Setting this to zero means that ray ranking is off, while 1 through 4 set the number of the evaluation iterations; 4 being 4 iterations. Setting this value to the max of 4 will slow down the reconstruction process but will produce more accurate results.
The Ray Ranking increases the stability of the reconstruction but at a heavy performance cost. The ray quality is analyzed by comparing convergence of rays that are contributing to the same marker. An average converging point is calculated, and each ray is ranked starting from the one closest to the converging point. Then, each ray is weighed differently in the Point Cloud reconstruction engine according to the assigned rankings.
This setting is useful especially when there are multiple rays contributing to a marker reconstruction. If you're working with small to medium marker counts, enabling this will not have an evident improvement on performance. Also, when precise real-time performance is required, disable this setting especially for a setup with numerous cameras.
Default: 0 pixels
Establishes a dead zone, in pixels, around the edge of the 2D camera image. Any 2D objects detected within this gutter will be discarded before calculating through the point cloud. In essence, it is a way of getting only the best data of the captured images, because markers seen at the edges of the camera sensor tend to have higher errors.
This setting can be increased in small amounts in order to accommodate for cases where lens distortions are potentially causing problem with tracking. Another use of the setting for limiting the amount of data going to the reconstruction solver, which may help when you have a lot of markers and/or cameras. Be careful adjusting this setting as the trimmed data can't be reacquired in post-processing pipelines.
Default: 5 degrees
The minimum allowable angle – in degrees from the marker's point of view – between the rays to consider them valid for marker reconstruction. This separation also represents the minimum distance required between the cameras. In general, cameras should be placed with enough distance in between in order to capture unique views on the target volume. For example, if there are only two cameras, an ideal reconstruction would occur when the cameras are separated far enough so the rays converge with a 90 degree of an incident angle from the perspective of the reconstructed marker(s).
When working with a smaller-sized system with a fewer number of cameras, there will be only a limited number of markers rays that can be utilized for reconstruction. In this case, lower this setting to allow reconstruction contributions from even the cameras that are in close vicinity to each other.
On the other hand, when working with a large system setup with a lot of cameras, you can set this value a bit higher to limit marker rays that are coming from the cameras that are too close together. Similar vantages obtained by the cameras within vicinity do not necessarily contribute unique positional data to the reconstruction, but they only increase the required amount of computation. Rays coming from very close cameras may increase the error in the reconstruction. Better reconstruction can only be achieved with a good, overall camera coverage (See Camera Placements).
When the Rigid Body Marker Override is set to True, Motive will replace observed 3D markers with the rigid body's solution for those markers. 3D tracking data of reconstructed and labeled trajectories will be replaced by the expected marker locations of the corresponding rigid body solve.
This is applicable only for rigid bodies using Ray-Based tracking, and when the Use Smart Markers is enabled.
When this feature is enabled, Motive uses expected marker locations from both the model solve and the trajectory history to create virtual markers. These virtual markers are not direct reconstructions from the Point Cloud engine. When the use of smart markers is enabled, rigid body and skeleton asset definitions will also be used in conjunction with 2D data and reconstructed 3D data to facilitate reconstruction of additional 3D marker locations to improve tracking stability. These virtual markers are created to make live data match recorded data in situations where model and history data helped to improve the live solve
More specifically, for rigid body tracking, Motive will utilize untracked rays along with the rigid body asset definition to replace the missing markers in the 3D data. In order to compute these reconstructions, the rigid body must be using the Ray-Based tracking algorithm. For skeleton tracking, only the asset definitions are used to approximate virtual reconstruction at the location where the occluded marker was originally expected according to the corresponding skeleton asset.
Using the asset definitions in obtaining the 3D data could be especially beneficial for accomplishing stable tracking of the assets in low camera count systems where all of the reconstructions may not always meet the minimum required tracked ray requirements.
Usage note. In 2.0, trajectories of virtually created markers on a skeleton segment may not get plotted on the graph view pane.
When set to true, Motive will recognize the unique illuminations from synchronized active markers and perform active labeling on its reconstructions. If you are utilizing our active marker solution, this must be set to true. For more information about active labeling, read through the Active Marker Tracking page.
Sets the required minimum number of frames without occlusion for a tracked marker to be recognized as the same reconstruction to form a trajectory. If a marker is hidden, or occluded, longer than the defined number of frames, then the trajectory will be truncated and the marker will become unlabeled.
Default: 0.06 m
To identify and label a marker from one frame to the next, a prediction radius must be set. If a marker location in the subsequent frame falls outside of the defined prediction radius, the marker will no longer be identified and become unlabeled.
For capturing relatively slow motions with tight marker clusters, limiting the prediction radius will help maintaining precise marker labels throughout the trajectory. Faster motions will have a bigger frame to frame displacement value and the prediction radius should be increased. When capturing in a low frame rate settings, set this value higher since there will be bigger displacements between frames.
After markers have been reconstructed in Motive, they must be labeled. Individual markers can be manually labeled, but the auto-labeler simplifies this process using the Assets. Rigid body and skeleton assets, created in Motive, saves their marker arrangement definitions and uses them to auto-label corresponding marker sets within the Take. The auto-labeling, is a process of associating 3D marker reconstructions in multiple captured frames by assigning marker labels within the defined constraints. After the labeling process, each of the labeled markers provides respective 3D trajectories throughout the Take.