Rigid Pose Estimation¶
If you haven’t set up the SDK yet, make sure to go through those directions first. You’ll need to add the Core library to the app before using the specific feature API or custom model. Follow iOS setup or Android setup directions.
Create AR experiences using Rigid Pose Estimation to track the position and pose of any object in real-world coordinates.
- 1. Add the dependencies via Gradle
- 2. Create a RigidPoseOnDeviceModel
- 2. Get a FritzVisionRigidPosePredictor
- 3. Create a FritzCVImage from an image or a video stream
- 4. Run prediction to get 2D Keypoints.
- 5. Infer 3D Pose from RigidPoseResult
- 6. Place an AR Object / 3D model in the real world using the inferred Pose.
Custom Pose Models
To create a custom pose model for an object, please contact us.
- All predictions / model inferences are made completely on-device.
- No internet connection is required to interpret images or video.
- No internet dependency means super-fast performance.
Runs on live video with a fast frame rate. Exact FPS performance varies depending on device, but it is possible to run this feature on live video on modern mobile devices.