# Point clouds

> Point clouds describe physical objects and spaces as 3D coordinates. Group points by object id and kind so Rowe can reason about fit, placement, stability, motion, and scene structure.

Source: https://fourechelon.com/docs/point-clouds · Machine-readable: https://fourechelon.com/openapi.json · Index: https://fourechelon.com/llms.txt

## Group points by object

A point cloud with no object ids is a shape, not a scene. Rowe reasons about relationships between things, so every group you want to ask a question about needs its own object_id. Tasks like fit and placement address objects by that id.

- object_id — how you refer to the group in a task
- kind — box, container, surface, or another hint about what it is
- points — the coordinates themselves, in one consistent unit

## Units are yours to keep straight

Coordinates are taken as given. There is no unit inference and no normalization, so a scene mixing millimetres and metres will produce confident, wrong measurements. Convert before you send.

## When to use CAD instead

Point clouds are the right input for scanned or sampled geometry. If you have the CAD file, send a cad_scene — it keeps exact volumes, faces, and dimensions that a sampled cloud can only approximate.

## Request fields

| Field | Type | Description |
| --- | --- | --- |
| model | string | Model id. Use rowe-1.0. |
| input.task_type | string | fit, stability, placement, embed, understand, reconstruct, predict, act, or optimize. |
| input.cad_scene | object | Native CAD scene with B-rep solids, drawing entities, dimensions, constraints, and optional feature_tree. Preferred for STEP, IGES, DXF, PDF, SVG, and draw.io workflows. |
| input.previous_cad_scene | object | Optional previous native CAD scene for compare and temporal tasks. |
| input.point_cloud_scene | object | Current 3D scene with one or more point cloud groups. |
| input.previous_point_cloud_scene | object | Optional previous scene for temporal prediction. |
| input.image / input.images | object | Optional raster frames, depth maps, or multi-view visual context. |
| input.sensor_readings | object | Optional IMU, GPS, BLE, WiFi, altimeter, or device state readings. |
| input.goal | object | Optional target state for action, routing, placement, or optimization tasks. |
| input.object_id | string | Primary object id used by fit, stability, placement, and tracking tasks. |
| input.secondary_id | string | Target or container object id for fit and placement tasks. |
| input.detail | string | Use compact to reduce structured prediction detail for small or latency-sensitive requests. |