# triposr
{% columns %}
{% column width="66.66666666666666%" %}
{% hint style="info" %}
This documentation is valid for the following list of our models:
* `triposr`
{% endhint %}
{% endcolumn %}
{% column width="33.33333333333334%" %} Try in Playground
{% endcolumn %}
{% endcolumns %}
## Model Overview
A transformer-based model designed for rapid 3D object reconstruction from a single RGB image, capable of generating high-quality 3D meshes in under 0.5 seconds on an NVIDIA A100 GPU.
## Setup your API Key
If you don’t have an API key for the AI/ML API yet, feel free to use our [Quickstart guide](https://docs.aimlapi.com/quickstart/setting-up).
## API Schema
## POST /v1/images/generations
>
```json
{"openapi":"3.0.0","info":{"title":"AIML API","version":"1.0.0"},"servers":[{"url":"https://api.aimlapi.com"}],"paths":{"/v1/images/generations":{"post":{"operationId":"_v1_images_generations","requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"model":{"type":"string","enum":["triposr"]},"image_url":{"type":"string","format":"uri","description":"The URL of the reference image."},"output_format":{"type":"string","enum":["glb","obj"],"default":"glb","description":"The format of the generated image."},"do_remove_background":{"type":"boolean","description":"Enables removing the background from the input image."},"foreground_ratio":{"type":"number","minimum":0.5,"maximum":1,"default":0.9,"description":"Ratio of the foreground image to the original image."},"mc_resolution":{"type":"integer","minimum":32,"maximum":1024,"default":256,"description":"Resolution of the marching cubes. Above 512 is not recommended."}},"required":["model","image_url"],"title":"triposr"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"data":{"type":"array","nullable":true,"items":{"type":"object","properties":{"url":{"type":"string","nullable":true,"description":"The URL where the file can be downloaded from."},"b64_json":{"type":"string","nullable":true,"description":"The base64-encoded JSON of the generated image."}}},"description":"The list of generated images."},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."}},"required":["credits_used"]}},"description":"Additional details about the generation."}}}}}}}}}}}
```
## Example
{% tabs %}
{% tab title="Python" %}
{% code overflow="wrap" %}
```python
import requests
import json # for getting a structured output with indentation
def main():
response = requests.post(
"https://api.aimlapi.com/v1/images/generations",
headers={
# Insert your AIML API Key instead of :
"Authorization": "Bearer ",
"Content-Type": "application/json",
},
json={
"model": "triposr",
"image_url": "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/mushroom.jpg",
},
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
```
{% endcode %}
{% endtab %}
{% tab title="JavaScript" %}
{% code overflow="wrap" %}
```javascript
async function main() {
const response = await fetch("https://api.aimlapi.com/v1/images/generations", {
method: "POST",
headers: {
// Insert your AIML API Key instead of
"Authorization": "Bearer ",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "triposr",
image_url: "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/mushroom.jpg",
}),
});
// Optional error handling (similar to raise_for_status)
if (!response.ok) {
const errorText = await response.text();
throw new Error(`Request failed: ${response.status} ${errorText}`);
}
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main().catch(console.error);
```
{% endcode %}
{% endtab %}
{% endtabs %}
Response
{% code overflow="wrap" %}
```json5
{
"model_mesh": {
"url": "https://cdn.aimlapi.com/flamingo/files/b/0a93d66e/obumlgU3dCJFg0B-km4Yi_model.glb",
"content_type": "application/octet-stream",
"file_name": "model.glb",
"file_size": 1291912
},
"timings": {
"prepare": 3.6838808893226087,
"generation": 0.0709337661974132,
"export": 0.6907037640921772
},
"remeshing_dir": null,
"requestId": "019d2ee5-582a-7c83-93f2-2fcbad002d43",
"meta": {
"usage": {
"credits_used": 182000
}
}
}
```
{% endcode %}
The example returns a textured 3D mesh in GLB file format.
For clarity, we took several screenshots of our mushroom from different angles in an online GLB viewer. As you can see, the model understands the shape, but preserving the pattern on the back side (which was not visible on the reference image) could be improved:
Compare them with [the reference image](https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/mushroom.jpg):
{% hint style="info" %}
Try to choose reference images where the target object is not obstructed by other objects and does not blend into the background. Depending on the complexity of the object, you may need to experiment with the resolution of the reference image to achieve a satisfactory mesh.
{% endhint %}
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.aimlapi.com/api-references/3d-generating-models/stability-ai/triposr.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
