Rigid Pose Estimation¶
Create AR experiences using Rigid Pose Estimation to track the position and pose of any object in real-world coordinates.
The 3D pose estimation described here is restricted to rigid bodies like a book or chair. It does not apply to soft or compound bodies like people. If you require 3D human pose estimation, please contact us about your use case.
- 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.