Autocalibrate Cameras using AprilTags in Intel® SceneScape#

This guide provides a step-by-step process for calibrating cameras in Intel® SceneScape using fiducial markers (AprilTags). This method ensures accurate tracking by estimating camera poses based on known marker positions.

By following this guide, you will:

  • Select and place AprilTags correctly.

  • Generate a scene floor plan with markers visible.

  • Configure Intel® SceneScape to auto-calibrate using AprilTags.

  • (Optionally) Add a 3D map for scene visualization.

This calibration method is ideal for fixed camera setups requiring precise positional accuracy.

Prerequisites#

Before You Begin, ensure the following:

  • Camera Setup: Cameras placed with a clear view of the scene.

  • Scene Created: Add cameras in Intel® SceneScape and set the detection model to -m apriltag.

  • SceneScape Installation: Installed and running.

Note: To switch from the default person detection model, replace retail with apriltag in docker-compose.yml.

Steps to Calibrate Using AprilTags#

1. Select and Print AprilTags#

  • Use this PDF to print tags from the tag36h11 family. Additionally, AprilTags can be generated or downloaded from the AprilTag-imgs GitHub repository.

  • Tags must be:

    • Printed the same size.

    • Unique (one ID per scene).

    • Detectable by the camera.

    • Flat, unwrinkled, and glare-free on the floor plane.

    • Large enough to be seen clearly from each camera.

For best results, print on material that prevents glare and test different sizes.

2. Test for Detectability#

Place sample tags in the scene and observe the camera feed. Tags should show bounding boxes when detected.

AprilTag Test Detections

Figure 1: Testing AprilTag visibility in camera feed.

3. Place AprilTags Throughout the Scene#

  • Distribute tags so each camera sees at least 4.

  • Avoid occlusions or placing tags too close together.

  • Fix tags in position to avoid movement during calibration.

Note: The same AprilTags can be visible in multiple camera views, but ensure at least 4 tags are detectable in each camera.

4. Generate Scene Floor Plan#

Use a phone/tablet with LiDAR or another method to scan the scene. Export as a .glb (glTF binary).

Creating a Top-Down Orthographic Floor Plan View#

  1. Open the .glb in Windows 3D Viewer.

  2. Switch to orthographic, top-down view.

  3. Export the image and determine pixels-per-meter.

Generate an Orthographic Image

Figure 2: Export top-down orthographic scene view.

Notes

  • Ensure AprilTags are clearly visible in the exported image.

  • It may be useful to enable the grid since it helps in determining the pixels per meter for the image (it is a 1-meter grid).

5. Configure Scene and Calibrate#

  1. Open Intel® SceneScape and edit the scene.

  2. Upload the orthographic image and enter the scale (pixels-per-meter).

  3. Set Calibration Type to Apriltag.

  4. Enter the physical size of the tags.

Update Scene Map

Figure 3: Upload scene image and set calibration method.

AprilTag dimensions

Figure 4: Enter AprilTag dimensions.

  1. Edit docker-compose.yml to enable the autocalibration service:

autocalibration:
  image: scenescape:<version>
  networks:
    scenescape:
  depends_on:
    - broker
    - ntpserv
    - pgserver
    - scene
  command: autocalibration --dbhost pgserver --ntp ntpserv --broker broker.scenescape.intel.com
  privileged: true
  environment:
    EGL_PLATFORM: "surfaceless"
  volumes:
    - vol-media:/home/scenescape/SceneScape/media
  secrets:
    - certs
    - django
  restart: on-failure

  1. Restart Intel® SceneScape (see Docker Compose Profiles for details on choosing profiles):

docker compose --profile controller down
docker compose --profile controller up

  1. Navigate to the camera page and click Auto Calibrate.

  2. Adjust opacity slider to verify alignment.

  3. Click Save Camera to finalize pose.

Auto Calibrate Camera

Figure 5: Auto Calibrate using AprilTags.

6. (Optional) Add 3D Map to Scene#

  1. Edit scene and upload the .glb to the Map 3d field.

  2. Set X Rotation to 90.0 to convert Y-up to Z-up.

  3. Adjust scale manually.

Tips:

  • .glb files are usually in meters.

  • Scale differences between 2D/3D often stem from incorrect pixels-per-meter.

Customizable Parameters#

Parameter

Purpose

Expected Values/Range

Calibration Type

Specifies the calibration method

AprilTag, Markerless

Tag Size

Physical width/height of AprilTags in meters

Positive float (e.g., 0.15)

Pixels/Per Meter

Scene scale to align 2D image with real-world size

Positive integer (e.g., 100)

Camera Model

Defines the camera projection model

Pinhole (recommended)

Project Frame

Overlay camera view frustum on 3D scene

Enabled, Disabled

Future Enhancements#

  • Tag placement validation with heatmaps.

  • Visual calibration accuracy scoring.

  • Easier 3D scan positioning tools.

Supporting Resources#