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MotionMaster 3D docs

Heavy Tracking

Heavy Tracking is the primary high-quality workflow for camera reconstruction and solved lens data. Use it when the shot needs strong solve quality, a specific distortion model, calibration/STMap export, masks, undistortion, dense point clouds, or mesh generation.

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Workflow steps

  1. Prepare footage in the Select Footage or Batch panel.
  2. Choose a simple preset or custom Heavy mode.
  3. Open Lens Distortion / STMap and choose a camera model; SIMPLE_RADIAL is the default.
  4. Leave parameters on Auto, or enter the complete manual parameter list in the exact order shown by the selected model.
  5. Enable Variable Focal Length for zoom shots so frames may use separate camera calibrations.
  6. Enable masks, undistortion, dense output, or mesh generation as required.
  7. Run Autotrack Heavy and monitor or cancel it from the Progress panel.

Important details

Heavy Tracking uses the bundled COLMAP/GLOMAP toolchain for feature extraction, matching, mapping, camera reconstruction, and optional image undistortion, then uses OpenMVS-related tools for dense and mesh stages.

After a solve, the Solved Result dropdown can target any Heavy result found in the active Import Results folder. Calibration and STMap exports always use that selected result instead of silently using the most recent run.

Related docs

Continue with the connected workflow

Lens Calibration and STMap Export

The Heavy Tracking lens workflow preserves the complete solved COLMAP camera model and exports portable calibration JSON plus float STMaps for undistorting the original plate and redistorting CG back into it.

AI Masks and Polygon Masks

The masking system helps Heavy Tracking ignore or isolate moving objects. It supports SAM2 AI mask layers, manually drawn polygon layers, keyframed polygon animation, and composed mask output.

Dense Point Clouds

Dense Point Cloud generation creates a high-detail colorized representation of the tracked scene from Heavy Tracking results. It is useful when sparse points are not enough to understand the environment.