Automate multi-view dataset generation for NeRF, 3DGS, and 3D reconstruction using AI agents

What You Can Do

With a single natural-language prompt, your AI agent can:

• Generate datasets at scale — create 100-view, 196-view, or 400-view training datasets across your entire model library
• Quote before committing — check the exact credit cost for any batch before spending
• Organize with tags — filter and target specific model subsets (e.g. "all models tagged validation-set")
• Download results programmatically — get ZIP URLs for automated training pipelines

No scripting required. Describe what you want in plain English — the agent handles the API calls.

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Setup

Add this to your MCP client configuration (Windsurf, Claude Desktop, Cursor, or any MCP-compatible host):

{
  "mcpServers": {
    "dxgl": {
      "serverUrl": "https://mcp.dx.gl/",
      "headers": {
        "Authorization": "Bearer dxgl_sk_..."
      }
    }
  }
}

Replace dxgl_sk_... with your API key from the Portal → API Keys.

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Your First Prompt

Paste this into your AI agent to verify everything works:

List my models and tell me how many I have.

The agent will call list_models and return a summary. If this works, you're connected.

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Prompt Examples

Generate a dataset for one model

Generate a 196×1024 hemisphere dataset for model Ab3kF9x2qL1m.

The agent calls create_render with output: "dataset", datasetQuality: "196x1024", coverage: "hemisphere", then polls get_render until done, and returns the download URL.

Generate datasets for all models with a tag

Generate 100×800 hemisphere datasets for every model tagged "validation-set". Quote the cost first and wait for my approval before proceeding.

The agent will:

1. Call list_models with tags: "validation-set" to find matching models
2. Call quote with the dataset configuration to show the total credit cost
3. Wait for your confirmation
4. Call create_batch_renders to submit all datasets at once
5. Poll get_render for each until complete
6. Return download URLs for all ZIPs

Full sphere coverage for a specific object

Create a 400×2048 full sphere dataset for my cash register model. I need maximum coverage for reconstruction.

The agent uses coverage: "sphere" for ±80° elevation range instead of the default hemisphere.

Check what's already been rendered

Show me all models that already have dataset renders, and which ones don't have any yet.

The agent calls list_models, then get_model for each to inspect render history, and categorizes them.

Budget planning

I have 200 models tagged "catalog". How many credits would it cost to generate 196×1024 hemisphere datasets for all of them?

The agent calls quote with 200 dataset renders at 16 credits each, checks your balance, and reports whether you have enough.

Import and generate in one go

Import this model from https://example.com/scan.glb, tag it "street-furniture", then generate a 196×1024 sphere dataset.

The agent chains ingest_model → waits for system preview → create_render with dataset settings → polls until done → returns the download URL.

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Dataset Tiers

Tier	Views	Resolution	Credits	Best for
100x800	100	800×800	1	Quick experiments, proof-of-concept
196x1024	196	1024×1024	4	Production training, best quality-to-cost ratio
400x2048	400	2048×2048	16	Maximum fidelity, large-scale reconstruction

Coverage

• Hemisphere (default) — upper hemisphere views, suitable for objects typically seen from above
• Full sphere — ±80° elevation range, captures undersides and all angles

Output ZIP Contents

Every dataset ZIP contains:

• images/ — RGB PNG frames (composited on white background)
• depth/ — 8-bit grayscale depth PNGs
• depth_16bit/ — 16-bit grayscale depth PNGs (65,536 levels)
• normals/ — world-space normal map PNGs
• masks/ — foreground/background alpha masks
• transforms.json — camera intrinsics + per-frame 4×4 transform matrices (nerfstudio / instant-ngp format)
• overview.webp — 4-quadrant contact sheet

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Available Tools

Tool	What it does
ingest_model	Import a 3D model from a URL
list_models	List models with tag and pagination filters
get_model	Get model details and all its renders
create_render	Create a single dataset render
create_batch_renders	Create multiple dataset renders atomically
get_render	Poll render status until complete
download_render	Get the download URL for a completed dataset ZIP
get_account	Check your credit balance
quote	Estimate credit cost before committing

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Tips

Use tags for organization

Tag models before generating datasets. This lets you target subsets efficiently:

Generate datasets for all models tagged "batch-3" — skip any that already have a 196×1024 dataset.

Quote first on large batches

The quote tool prevents surprises. Ask the agent to quote before any batch operation:

Quote 196×1024 datasets for my 50 furniture models, then proceed only if it's under 200 credits.

Combine with nerfstudio

After downloading dataset ZIPs, train with nerfstudio:

unzip dataset.zip -d mymodel
ns-train splatfacto --data ./mymodel \
  --max-num-iterations 15000 \
  --pipeline.model.sh-degree 3 \
  --pipeline.model.background-color white

The background-color white flag is required — images are composited on a white background.

Automate the full pipeline

Your agent can orchestrate the entire workflow in one prompt:

Import all GLB files from these URLs, tag them "experiment-7", generate 196×1024 sphere datasets for each, and give me the download URLs when done.

The agent handles import → tagging → rendering → polling → URL collection without any manual steps.

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Learn More

• API Documentation — Full REST API reference
• Portal Guide — How to use the DX.GL portal
• For Research — Multi-view datasets for 3D reconstruction
• Integration Guide — Build on top of DX.GL (white-label, platform)
• Free Dataset Samples — Browse and download CC0 datasets