# HALOWERK OpenCV Landmark Camera Pose Estimation

> 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.

## Facts

- Endpoint: POST https://medien.halowerk.com/v1/oss-opencv-landmark-camera-pose?utm_source=zero.xyz
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/halowerk-opencv-landmark-camera-pose-estimation-0b7fcbc8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lrrByZRcdLxBwugVAHxnR

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability halowerk-opencv-landmark-camera-pose-estimation-0b7fcbc8 -d '<json body>'
```

Example prompt: I have 3D coordinates of 8 landmarks on a known object, their matching 2D pixel positions in an image, the 3x3 camera intrinsic matrix, and 5 distortion coefficients — can you run OpenCV's solvePnP on these and give me the rotation vector, translation vector, and reprojection RMSE?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "distortion": {
   "type": "array",
   "items": {
    "type": "number",
    "maximum": 1000000000,
    "minimum": -1000000000
   },
   "maxItems": 5,
   "minItems": 5
  },
  "image_points": {
   "type": "array",
   "items": {
    "type": "array",
    "items": {
     "type": "number",
     "maximum": 1000000000,
     "minimum": -1000000000
    },
    "maxItems": 2,
    "minItems": 2
   },
   "maxItems": 2048,
   "minItems": 6
  },
  "camera_matrix": {
   "type": "array",
   "items": {
    "type": "array",
    "items": {
     "type": "number",
     "maximum": 1000000000,
     "minimum": -1000000000
    },
    "maxItems": 3,
    "minItems": 3
   },
   "maxItems": 3,
   "minItems": 3
  },
  "object_points": {
   "type": "array",
   "items": {
    "type": "array",
    "items": {
     "type": "number",
     "maximum": 1000000000,
     "minimum": -1000000000
    },
    "maxItems": 3,
    "minItems": 3
   },
   "maxItems": 2048,
   "minItems": 6
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/halowerk-opencv-landmark-camera-pose-estimation-0b7fcbc8/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from medien.halowerk.com](https://www.zero.xyz/host/medien.halowerk.com/llms.txt)