This page explains how 3D coordinates are derived from captured camera images through the reconstruction process. A clear understanding of this process will allow you to fully utilize Motive for analyzing and optimizing captured 3D tracking data. Once the basic concept of the reconstruction process has been covered, we will go over some tips and instructions on how to inspect and optimize the configurations for best tracking results.
Reconstruction in motion capture is a process of deriving 3D points from 2D coordinates obtained by captured camera images, and the Point Cloud Reconstruction Engine is the core engine that runs this process. When multiple synchronized images are captured, 2D centroid locations of detected marker reflections are triangulated frame-by-frame in order to obtain respective 3D positions within the calibrated capture volume.
The Reconstruction pane contains all of the settings and filter options for the reconstruction engine, and you can modify the parameters to optimize the quality of reconstructions. Note that optimal configurations may vary depending on capture applications and environment conditions. For most common applications, default settings should work fine, but still, understanding these settings will allow you to fully utilize the 3D reconstruction capabilities in Motive.
In this page, we will focus on the Reconstruction Settings and the Camera Settings, which are the key settings that have direct affects on the reconstruction outcome.
Camera settings can be configured under the Devices pane. In general, the overall quality of 3D reconstructions is affected by the quality of captured camera images. Thus, the camera settings, such as camera exposure and IR intensity values, must always be optimized for capturing clear images of tracked markers. The following sections highlight some of the settings that are directly related to 3D reconstruction.
When cameras are set to tracking mode, only the pixels with brightness values greater than the configured THR setting are captured and processed. These pixels are referred as thresholded pixels, and all other pixels that do not satisfy the brightness get filtered out. Clusters of thresholded pixels are then filtered through the 2D Object Filter to be potentially considered as marker reflections.
We do not recommend lowering the THR value (default:200) for the cameras since lowering THR settings can introduce false reconstructions and noise in the data
To inspect brightness values of the pixels, set the Pixel Inspection to true under the View tab in the Application Settings.
The Reconstruction pane contains settings for controlling the Point Cloud reconstruction in Motive. When a camera system captures multiple synchronized 2D frames, the images are processed through two main filter stages before getting reconstructed into 3D data. The first filter is on the camera hardware level and the other filter is on the software level, and both of them are important in deciding which 2D reflections get identified as marker reflections and be reconstructed into 3D data. Adjust these settings to optimize the 3D data acquisition in both live-reconstruction and post-processing reconstruction of capture data.
When a frame of image is captured by a camera, the 2D Object Filter is applied. By judging on sizes and shapes of the detected reflections, this filter determines which of them can be accepted as marker reflections. Parameters for the 2D Object Filter are configured under the 2D Object Filter section of the Reconstruction pane.
The Min/Max Thresholded Pixels settings determine lower and upper boundaries of the size filter. Only reflections with pixel counts within the boundaries will be considered as marker reflections, and any other reflections below or above the defined boundary will be filtered out. Thus, it is important to assign appropriate values to the minimum and maximum thresholded pixel settings.
For example, in a close-up capture application, marker reflections appear bigger on camera's view. In this case, you may want to lower the maximum threshold value to allow reflections with more thresholded pixels to be considered as marker reflections. For common applications, however, the default range should work fine.
Enable Marker Size under the visual aids () in the Camera Preview viewport to inspect which reflections are accepted, or omitted, by the size filter.
In addition to the size filter, the 2D Object Filter also identifies marker reflections based on their shape; specifically, the roundness. It assumes that all marker reflections have circular shapes and filters out all non-circular reflections detected by each camera. The allowable circularity value is defined under the Marker Circularity settings in the Reconstruction pane. The valid range is between 0 and 1, with 0 being completely flat and 1 being perfectly round. Only reflections with circularity values bigger than the defined threshold will be considered as marker reflections.
Enable Marker Circularity under the visual aids in the Camera Preview viewport to inspect which reflections are accepted, or omitted, by the circularity filter.
The Object Mode and Precision Mode deliver slightly different data to the host PC. In the object mode, cameras capture 2D centroid location, size, and roundness of markers and deliver to the host PC. In the precision mode, cameras capture only centroid region of interests. Then, this region is delivered to the host PC for additional processing to determine the centroid location, size, and roundness of the reflections. Read more about Video Types.
After the 2D object filter has been applied, each of 2D centroids captured by each camera forms a marker ray, which is a 3D vector ray that connects a detected centroid to a 3D coordinate in a capture volume. When a minimum of 2 rays (defined by Minimum Rays) converge and intersect within the allowable maximum offset distance (defined by Maximum Residual settings), reconstruction of a 3D marker occurs.
Monitoring marker rays is an efficient way of live-inspecting reconstruction outcomes. To visualize these marker rays, Motive must be live-reconstructing from either live or recorded 2D data. Then, the rays must be enabled for viewing under the visual aids options in the Perspective View pane. The live-reconstruction, which will be covered later in this page, occurs in either the Live mode or the 2D mode. There are three different types of marker rays that are visualized in Motive:
There are three different types of marker rays in Motive: tracked rays, untracked rays, and missing rays. By inspecting these marker rays, you can easily find out which cameras are contributing to a marker.
Motive processes markers rays with camera calibration to reconstruct respective 3D coordinates, and here, another stage of filtering is applied. When marker rays converge into a 3D point, the Point Cloud reconstruction engine refers to the reconstruction parameters and determines which sets of converging rays are acceptable to be reconstructed into 3D data. These parameters are defined under the reconstruction tab in the Application Settings, and only those that do not qualify the given conditions will be occluded. Some of the commonly modified key parameters are summarized below.
The maximum allowable offset distance (in mm) between the converging rays contributing to a 3D reconstruction. This distance is referred as the residual distance in Motive. When a marker ray converges on a set of other rays with a residual distance larger than the defined maximum value, the ray will not contribute to reconstruction of the 3D point. Lower this setting if you want 3D markers to be reconstructed only when marker rays are precisely converging onto a 3D point. For larger capture volume setups, increase this value to be more lenient on reconstructions that have bigger residual offsets.
When you select a marker in the live-reconstruction mode, a respective residual value will be displayed on the status bar. Smaller residual values represents precisely converged tracked rays and are more accurate representation of 3D coordinates.
When calibration quality of a camera system is degraded, the residual value of the system will increase.
This setting sets a minimum number of tracked marker rays required for a 3D point to be reconstructed. In other words, this is the required number of calibrated cameras that need to see the marker. Increasing the minimum ray count may prevent extraneous reconstructions, and decreasing it may prevent marker occlusions from not enough cameras seeing markers. In general, modifying this is recommended only for high camera count setups.
There are other reconstruction setting that can be adjusted to improve the acquisition of 3D data. For detailed description of each setting, read through the Reconstruction Settings page.
Live-reconstruction is a real-time reconstruction of 3D coordinates directly from captured, or recorded, 2D data. To allow Motive to live-reconstruct, the Enable Point Cloud Reconstruction must be enabled under the Reconstruction tab in the Application Settings pane. When Motive is live-reconstructing, you can examine the marker rays from the viewport, inspect the point cloud reconstruction, and optimize the 3D data acquisition.
There are two modes where Motive is live-reconstructing:
In the Live Mode, Motive is reconstructing from captured 2D frames in real-time, and you can inspect and monitor the marker rays from the perspective vew. Any changes to the Point Cloud reconstruction settings will be reflected immediately in the Live mode.
The 2D Mode is used to monitor 2D data in the post-processing of a captured Take. When Motive records a capture, both 2D camera data and reconstructed 3D data are saved into a Take file, and by default, the latter is always loaded first when a recorded Take file is opened.
Recorded 3D data contains only the 3D coordinates that were live-reconstructed at the moment of capture; in other words, this data is completely independent of the 2D data once a capture has been recorded. You can still, however, view and use the recorded 2D data to optimize the Point Cloud parameters and reconstruct a fresh set of 3D data from it. To do so, you need to switch into the 2D Mode in the Data Management pane. In the 2D Mode, you will be able to inspect the reconstructions and marker rays from the viewports. In the 2D Mode, Motive is live-reconstructing from recorded 2D data and any changes to the reconstruction settings will be reflected.
Once the reconstruction parameters have been optimized, the post-processing reconstruction pipeline needs to be performed on the Take in order to reconstruct a new set of 3D data. Here, note that the existing 3D data will get overwritten and all of the post-processing edits on it will be discarded.
The post-processing reconstruction pipeline allows you to convert 2D data from recorded Takes into 3D data. In other words, you can obtain a fresh set of 3D data from recorded 2D camera frames by performing reconstruction on a Take. Also, if any of the Point Cloud reconstruction parameters have been optimized using the 2D Mode in post-processing, the changes will be reflected on the newly obtained 3D data.
Once 3D markers have been reconstructed per frame basis, they need to be linked between every consecutive frame.
This process is included within the reconstruction process
and all of the reconstructed trajectories get saved into the 3D data.
This is achieved by forming marker Trajectories.
Marker trajectories are correlated reconstructions that are assumed to be the same reconstructions but exist on the consecutive frames.
Basically, when reconstructed 3D points from consecutive frames are located within the prediction radius' of each other and do not get occluded for more than the maximum gap frame, they are correlated together and a trajectory is formed.
In other words, a trajectory is a 3D path created from a set of interconnected reconstructions from recorded frames.
Trajectories are available only in recorded 3D data. Once Trajectories are formed, path history of each marker label can be accessed, and change in positions over time can be viewed from the viewports or from the Graphs View pane.
Furthermore, labels can be assigned to each trajectory. Marker labels are basically software name tags that are assigned to trajectories so that they can be referenced for tracking individual markers, rigid bodies, or skeletons. Labels are automatically assigned to trajectories that consist of reconstructions that are associated with trackable assets that are active in the Assets pane. For those that are not associated with any of the assets, they need to be manually labeled. Labeling in Motive will be further discussed in the Labeling page.
The prediction radius, in mm, is defined in the Auto-labeler settings under the reconstruction settings.
In case of marker occlusions, the occluded frames can be accommodated in a trajectory as long as a reconstruction reappears within the prediction radius before exceeding the number of allowable gap frames which is defined by the Minimum Key Frames setting.