# Point Cloud Denoiser MCP

> Point Cloud Denoiser MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.35/call, status unknown (last checked 2026-09-15).

Removes noise from 3D point cloud data to produce cleaner, more accurate spatial datasets

## Facts

- Endpoint: POST https://api.m2mcent.com/point-cloud-denoiser-mcp/api/process
- Price: $0.35/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/point-cloud-denoiser-mcp-35366d07
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FELKUQqLhX4stP8gV54RD

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 point-cloud-denoiser-mcp-35366d07 -d '<json body>'
```

Example prompt: Can you clean up this noisy LiDAR point cloud data I have — it has a lot of outlier points and scan artifacts that need to be removed before I process it further?

## When to prefer this

Choose this endpoint when you need to preprocess noisy 3D scan data (LiDAR, photogrammetry, depth sensors) before downstream tasks like mesh reconstruction, object detection, or spatial analysis. Useful when raw sensor data contains outliers, artifacts, or statistical noise that would degrade model accuracy.

## Known failure modes

- Malformed or invalid point cloud data format returns an error
- Data string too large may cause timeout or processing failure
- Empty data field results in no output or error response
- Unsupported point cloud encoding or format may not be processed correctly

## 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

A processed, denoised point cloud with noise points and outliers removed, returned as cleaned spatial data ready for downstream 3D processing, reconstruction, or analysis tasks.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "payload": {
   "type": "string"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "success": true
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/point-cloud-denoiser-mcp-35366d07/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.m2mcent.com](https://www.zero.xyz/host/api.m2mcent.com/llms.txt)