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HALOWERK OpenCV Landmark Camera Pose Estimation
x402
HALOWERK OpenCV Landmark Camera Pose Estimation is a paid API for AI agents from medien.halowerk.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-30).
Solves for the 6-DoF rigid transform (rotation + translation) between an object coordinate frame and the camera using 3D-to-2D landmark correspondences, returning the pose vectors and reprojection RMSE.
Price / call$0.002
Success—
StatusUnknown
How this service works
Solves for the rigid transform between an object's coordinate frame and the camera, given 3D landmark coordinates, where those landmarks appear in the image, and the camera matrix and distortion. Returns the rotation and translation together with the reprojection RMSE, so a caller can tell a well-constrained pose from one that merely converged. This is the standard step for placing a known object in a scene or locating a camera against a known marker.
Output
Returns the solved rotation vector (Rodrigues, 3 elements), translation vector (3 elements) expressing the object-to-camera rigid transform, and the reprojection RMSE in pixels indicating how well the estimated pose reprojects the 3D points back onto the image plane.
Example use cases
AR object placement in scene
“I'm building an AR app and need to place a virtual overlay on a physical box. I have the 3D corners of the box in object space, their 2D pixel locations in my camera image, the camera intrinsic matrix, and distortion coefficients — can you estimate the box's pose relative to the camera and tell me the reprojection error so I know if the solution is reliable?”
Fiducial marker localization for robotics
“I have a known 3D model of a fiducial marker mounted in a robot workspace, I've detected 10 of its landmarks in an image and recorded their pixel coordinates, and I have the camera matrix and distortion params — please run solvePnP and return the rotation and translation so my robot can compute its position relative to the marker.”
Camera extrinsic calibration from known target
“I've got a calibration target with 12 known 3D points and their corresponding 2D detections in an image, along with the camera intrinsics and distortion — compute the rotation and translation vectors that describe the camera's pose relative to the target and give me the reprojection RMSE so I can validate the calibration quality.”
Technical details
Endpoint
POSThttps://medien.halowerk.com/v1/oss-opencv-landmark-camera-pose?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a remote, pay-per-call OpenCV solvePnP implementation without managing a local OpenCV environment. Ideal for agents or serverless pipelines that already have 3D-2D correspondences, camera intrinsics, and distortion coefficients ready and need a reliable pose solve with reprojection quality feedback. Prefer over manual deployment when cost per call ($0.002 USDC) is acceptable and avoiding infrastructure overhead matters.
Side effects
- Fewer than 6 point correspondences provided — rejected by schema validation
- Degenerate or coplanar point configurations causing solvePnP to fail or produce unstable results
- Camera matrix not a valid 3x3 array — schema error
- Distortion array not exactly 5 elements — schema error
- High reprojection RMSE indicating a poorly constrained or incorrect pose — returned in result for caller to evaluate
- Mismatched number of object_points and image_points causing solver failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| distortion | array | |
| image_points | array | |
| camera_matrix | array | |
| object_points | array |
Tags
x402
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