Build a 3D model from 2–4 ordered front, left, back, and right reference images with configurable output options.
Related Tripo3D H3.1 Models
API Examples
Submit Example
const response = await fetch('https://api.flaq.ai/api/v1/3d/task', {
method: 'POST',
headers: {
Authorization: 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model_name: 'tripo3d-h3.1-multiview-to-3d',
images: [
'https://example.com/front.png',
'https://example.com/left.png',
'https://example.com/back.png',
'https://example.com/right.png',
],
texture_alignment: 'original_image',
orientation: 'default',
texture: true,
pbr: true,
texture_quality: 'standard',
geometry_quality: 'standard',
auto_size: false,
quad: false,
}),
});
const { data } = await response.json();
const taskId = data.task_id;
Polling Example
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/3d/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.files[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}
Submit Example
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/3d/task',
headers={
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
json={
'model_name': 'tripo3d-h3.1-multiview-to-3d',
'images': [
'https://example.com/front.png',
'https://example.com/left.png',
'https://example.com/back.png',
'https://example.com/right.png',
],
'texture_alignment': 'original_image',
'orientation': 'default',
'texture': True,
'pbr': True,
'texture_quality': 'standard',
'geometry_quality': 'standard',
'auto_size': False,
'quad': False,
},
)
response.raise_for_status()
result = response.json()
task_id = result['data']['task_id']
Polling Example
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/3d/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['files'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)
Submit Example
curl -X POST "https://api.flaq.ai/api/v1/3d/task" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model_name": "tripo3d-h3.1-multiview-to-3d",
"images": [
"https://example.com/front.png",
"https://example.com/left.png",
"https://example.com/back.png",
"https://example.com/right.png"
],
"texture_alignment": "original_image",
"orientation": "default",
"texture": true,
"pbr": true,
"texture_quality": "standard",
"geometry_quality": "standard",
"auto_size": false,
"quad": false
}'
Polling Example
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/3d/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
Try an example
Tripo H3.1 Multiview to 3D Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
High-Fidelity Tripo H3.1 Multiview-to-3D API (AI 3D Asset Generation from Multiple Views)
Tripo H3.1 Multiview-to-3D API by Tripo3D AI generates detailed 3D assets from a coherent set of reference images of the same object. By using multiple viewing angles, the Tripo H3.1 model can build a more informed interpretation of the object’s geometry and surface appearance for production-oriented 3D workflows. Flaq AI makes this multiview 3D generation capability available through an API for product visualization, game assets, digital twins, and high-detail creative production.
Key Features of Tripo H3.1 Multiview-to-3D API
- Multi-Angle 3D Reconstruction: Generate a 3D asset from multiple views of the same subject, helping the model use broader visual coverage when forming geometry and texture details.
- High-Fidelity Geometry Generation: Create detailed meshes for assets intended for close-up renders, interactive scenes, product showcases, and other demanding 3D applications.
- Reference-Aware Texture Output: Produce optional textures and PBR materials while choosing whether the result should favor source-image appearance or coherent geometric interpretation.
- Flexible Production Controls: Match geometry, texture, sizing, and mesh preferences to the needs of a polished render, a downstream editing workflow, or a more structured asset handoff.
- Option-Based Cost Control: Billing reflects the selected output options, combining a base generation credit with applicable texture, detailed-geometry, and quad-mesh surcharges.
- Scalable API Integration: Bring multiview-to-3D generation into asset libraries, creative applications, visualization systems, and automated Flaq AI workflows.
How to Use Tripo H3.1 Multiview-to-3D API for Detailed 3D Reconstruction on Flaq AI
- Input: Multiple images of the same object captured from complementary angles, with a consistent subject and visual presentation across the set.
- Output: A generated 3D mesh with optional textures and PBR materials, returned through the Flaq AI API workflow for rendering, editing, or delivery.
- Reference Direction: Control whether generated surface output should follow the supplied images closely or give greater priority to coherent 3D geometry.
- Asset Preparation: Flexible fidelity, orientation, scale, and topology options support different downstream production requirements.
- Capabilities: Multiview 3D reconstruction, high-detail geometry, reference-aware texture output, PBR material generation, and option-based asset delivery through Tripo H3.1.
Best Use Cases for Tripo H3.1 Multiview-to-3D API Integration
- Product Digital Twins: Turn a curated set of product views into detailed 3D assets for online configuration, interactive commerce, and visual product experiences.
- Game and Entertainment Assets: Build a strong starting point for props, collectibles, and hero objects when art teams can provide several views of the same design.
- Architecture and Interior Visualization: Generate reusable 3D elements from multiple reference images for staging, concept visualization, and detailed scene construction.
- Marketing and CGI Production: Reconstruct campaign objects for 3D product films, close-up renders, immersive launch materials, and branded digital experiences.
- Asset Pipeline Automation: Add multiview reconstruction to internal tools and content platforms that need consistent, repeatable API-based 3D generation.
Note Use multiple images of the same object under consistent lighting and with clear, complementary angles. Ensure that you have the necessary rights to use every reference image.
Tripo H3.1 Multiview-to-3D vs Competitors: Comparative Analysis
-
Tripo H3.1 Multiview-to-3D vs. Tripo H3.1 Image-to-3D: Multiview-to-3D uses broader visual coverage and is well suited to objects that benefit from several reference angles. Image-to-3D is the faster path when a single strong image is all that is available.
-
Tripo H3.1 Multiview-to-3D vs. Tripo H3.1 Text-to-3D: Multiview-to-3D is designed to reconstruct a specific referenced object. Text-to-3D offers more creative freedom when the asset has not yet been visualized.
-
Tripo H3.1 Multiview-to-3D vs. Meshy Image-to-3D: Meshy can support rapid image-guided asset creation. Tripo H3.1 Multiview-to-3D API is tailored to workflows that can provide multiple views and want to make fuller use of the available reference information.
-
Tripo H3.1 Multiview-to-3D vs. Rodin 3D Generation: Rodin is widely used for AI-generated 3D assets from reference imagery. Tripo H3.1 emphasizes high-fidelity, multi-angle reconstruction with material-aware output and flexible production controls in Flaq AI workflows.
-
Tripo H3.1 Multiview-to-3D vs. Conventional Photogrammetry: Conventional photogrammetry can require extensive capture, cleanup, and reconstruction work. Tripo H3.1 provides an API-driven alternative for teams that want to generate an asset from a curated set of object views.