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Poisson Surface Reconstruction MCP
x402
Poisson Surface Reconstruction MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.35/call, status unknown (last checked 2026-09-14).
Reconstructs a smooth 3D mesh surface from unstructured point cloud data using the Poisson surface reconstruction algorithm.
Price / call$0.35
Success—
StatusUnknown
How this service works
The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.
Output
Returns a reconstructed 3D surface mesh derived from the input point cloud, typically as mesh geometry data (vertices, faces, or a standard mesh format), representing a smooth, watertight surface fitted to the input points.
Example use cases
3D scan to printable mesh
“I have a raw point cloud exported from my 3D scanner — can you run Poisson surface reconstruction on it so I get a clean, watertight mesh I can send to the 3D printer?”
LiDAR terrain surface generation
“Here's my LiDAR point cloud data from a terrain survey — reconstruct a smooth surface mesh from it using Poisson reconstruction so I can visualize the terrain geometry.”
Depth camera output to 3D model
“I captured depth sensor data from my RGB-D camera and have it as a point cloud — please reconstruct a solid 3D surface mesh from it using Poisson surface reconstruction.”
Technical details
Endpoint
POSThttps://api.m2mcent.com/poisson-surface-reconstruction-mcp/api/processWhen to prefer
Choose this endpoint when you need to convert unstructured 3D point cloud data into a smooth, watertight polygonal mesh using the Poisson surface reconstruction algorithm — especially useful for 3D scanning, LiDAR data, depth camera output, or any scenario where raw spatial point samples need to be turned into renderable or printable 3D geometry.
Side effects
- Malformed or empty point cloud data string returns an error
- Insufficient point density may produce a poor-quality or incomplete mesh
- Very large point clouds may exceed processing limits or time out
- Non-serializable or incorrectly encoded data may cause a parse failure
- Missing required 'data' field returns a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| payload | string |
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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