Settings: Live Reconstruction
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v2.3
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The real-time reconstruction settings can be accessed in the Reconstruction tab under the 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 page.
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.
Enable Point Cloud Reconstruction
Default: ON
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.
Choosing a good Residual Value
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.
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.
Default: Passive
Default: 12
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.
Default: Disabled
Default: 4
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.
Default: False
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.
Default: True
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
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.
Default: true
Default: 20
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.
Bound Reconstrutction
(Default: False) When set to true, the 3D points will be reconstructed only within the given boundaries of the capture volume. The minimum and maximum boundaries of X/Y/Z axis are defined in the below properties.
Visible Bounds
Bounds Shape
(Default: Cuboid) This setting selects the shape of the reconstruction bound. You can select from cuboid, cylinder, spherical, or ellipsoid shapes and the corresponding size and location parameters (e.g. center x/y/z and width x/y/z) will appear so that the bound can be customized to restrict the reconstruction to a certain area of the capture volume.
Pose Detection
Default: TruePose detection improves the stability of skeleton tracking by detecting standing poses. For multi-skeleton captures, this feature may increase the skeleton solve latency.
Minimum Key Frames
Default: 2This setting sets the required minimum number of frames for each trajectory in the recorded 3D data. Any trajectories with a length less than the required minimum will be discarded from the 3D data after running the auto-labeling pipeline.
Auto-labeler Passes
Default: 1The number of iterations for analyzing detected marker trajectories for maintaining constant marker labels. Increasing this setting can improve the marker auto-labeling, but more iterations will require more time and computation effort to complete the auto-labeling.
Rigid Body Assisted Labeling can be used to optimize the labeling of markers within a region defined by a rigid body. The first step in using this feature is to create a rigid body from markers that are visible and rigidly connected. The example shown in the figure below demonstrates this for hand tracking. Five white markers are selected on the top of the wrist - which is rigidly defined. The black markers on the fingers are not rigidly defined in any fashion but are within the boundary of the Rigid Body Assisted Labeler. Labeling continuity is improved for the markers on the fingers which are given automatic labels.Tracking of organic or flexible objects - that do not have a tracking models like the face and hand, are good candidates for Rigid Body Assisted Labeling.
Rigid Body-Assisted Labeling
Default: FalseEnable or disable rigid body assisted labeling feature.
Rigid Body Volume Radius
Default: 300 mmThe rigid body volume radius defines the region of space where the rigid body assisted labeling is applied. Increasing this radius will increase time needed for the auto-labeling so care should be made when setting this property.
Prediction Radius (mm)
Default: 10 mmThe prediction radius defines the size of the bounding region used to label markers. When labeling a marker from one frame to the next, a bounding region, relative to the rigid body, is created around each labeled marker. The labeling continuity is restricted to the bounding region from frame to frame. Increasing this can allow markers to swap if there are occlusions in the data. Decreasing this restricts labeling from frame to frame but may lead to an increase in broken trajectories.
Maximum Assisted Labeling Gap
Default: 30 framesThe maximum gap frames property defines the maximum number of frames a marker can be hidden before it is truncated or unlabeled. Increase this value if larger gaps are to be anticipated. Increasing the assisted labeling gap will increase the processing time of reconstruction.
Discard External Markers
Default: FalseDiscards markers outside of rigid body volume. Enabling this property will eliminate marker reconstructions outside of the region defined by the Rigid Body Volume.
Dynamic Constraints
Default: NonePrevents the rigid body from moving/rotation more than specified amount per frame.
Max Translation (mm)
Default: 100Distance for Dynamic Translation Constraint option.
Max Rotation (deg)
Default: 30Angle for the Dynamic Rotation Constraint option.
Minimum Tracking Frames
Default: 20Dynamic constraints are enabled when the rigid body is consecutively tracking more than this frame count.
Marker Filter Diameters
Default: FalseMarkers less than this diameter will not be used for rigid body tracking.
Minimum Diameter (mm)
Default: 10Diameter used for Marker Filter Diameter option.
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 ). 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 when the tracked rays and untracked rays are enabled from the visibility settings.
This toggles on and off. It is recommended to turn this off if computer resource need to be dedicated to 2D recording. When disabled, you will not be able to see 3D data from the Live mode nor from the recorded 2D data.
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 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 ).
Configures Motive for tracking either the passive markers, the synchronized active markers, or both. See for more information.
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 page.
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 ).
This is applicable only for rigid bodies using , and when the Use Smart Markers is enabled.
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 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.
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 page.
Visualize the reconstruction bounds in the .
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 . 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 , 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.
