Tengpaz/worldrenderer-dataset-test

WorldRenderer Dataset Test

Dataset Summary

WorldRenderer Dataset Test is a synthetic multi-scene 3D rendering dataset designed for research in:

• —Novel View Synthesis (NVS)
• —Neural Rendering
• —Geometry-aware Generation
• —Multi-view Representation Learning
• —World Models
• —3D-conditioned Generative Modeling

The dataset contains 27 textured 3D scenes. Each scene is rendered using a predefined monocular camera trajectory consisting of 401 frames. For every frame, aligned multi-modal rendering outputs are provided, including:

• —RGB images
• —Depth maps
• —Surface normal maps

Additionally, each scene also contains:

• —the original textured 3D scene asset
• —the first rendered frame
• —an automatically generated caption for the first frame

This dataset is intended to provide aligned geometric and visual supervision for modern multi-view generation and rendering systems.

Dataset Characteristics

Supported Tasks

This dataset can be used for:

• —Novel View Synthesis
• —Neural Rendering
• —Multi-view Diffusion Models
• —Geometry-aware Image Generation
• —3D Reconstruction
• —Surface Normal Estimation
• —Depth Prediction
• —Camera-conditioned Generation
• —World Modeling

Dataset Structure

Each scene is stored in an independent folder. Example directory structure:

worldrenderer-dataset-test/
├── 0000/
│   ├── rgb/
│   │   ├── rgb_000000.png
│   │   ├── rgb_000001.png
│   │   └── ...
│   │
│   ├── depth/
│   │   ├── depth_000000.png
│   │   ├── depth_000001.png
│   │   └── ...
│   │
│   ├── normal/
│   │   ├── normal_000000.png
│   │   ├── normal_000001.png
│   │   └── ...
│   │
│   ├── first_frame.png
│   ├── model.blend
│
├── 0001/
│   └── ...
│
├── ...
│
└── model.tar.gz

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Naming Convention

All frame indices start from 0.

Frame naming format:

rgb000000.png depth000000.png normal_000000.png

The final frame index is:

000400

corresponding to a total of 401 frames per scene.

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Data Fields

RGB

Rendered RGB images along the camera trajectory.

Example:

rgb/rgb_000123.png

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Depth

Depth maps aligned with RGB images.

Example:

depth/depth_000123.png

Depth values are rendered directly from the 3D scene geometry.

1mm metric

65.535m max depth

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Normal

Surface normal maps aligned with RGB images.

Example:

normal/normal_000123.png

Normals are represented in camera space.

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Model

Original textured 3D scene asset.

model/

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Data Generation Pipeline

For each scene:

1. 1.A textured 3D environment is loaded.
2. 2.A monocular camera trajectory is generated.
3. 3.401 aligned frames are rendered.
4. 4.RGB, depth, and normal maps are exported.
5. 5.All assets are organized into scene-wise folders.

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Intended Uses

The dataset is designed for research purposes, including:

• —training neural rendering systems
• —studying geometry-aware generation
• —evaluating multi-view consistency
• —camera-conditioned generation
• —3D scene understanding
• —synthetic world modeling

Potential model families include:

• —NeRF-based methods
• —Gaussian Splatting pipelines
• —Diffusion Transformers
• —Multi-view autoregressive models
• —Geometry-aware diffusion models
• —Vision-language world models

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Example Usage

Basic File Access

from pathlib import Path root = Path("worldrenderer-dataset-test") scenedir = root / "0000" rgbpath = scenedir / "rgb" / "rgb000000.png" depthpath = scenedir / "depth" / "depth000000.png" normalpath = scenedir / "normal" / "normal000000.png" print(rgb_path)

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Recommended Research Directions

This dataset is particularly suitable for:

• —M-to-N view generation
• —Geometry-conditioned diffusion
• —Camera-conditioned transformers
• —Unified rendering and reconstruction
• —World representation learning
• —Multi-modal scene understanding

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Limitations

• —Synthetic rendering only
• —Limited scene diversity
• —Single trajectory per scene
• —Automatically generated captions may contain inaccuracies
• —Rendering configuration may differ from real-world camera distributions

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Contact

For questions, issues, or collaboration opportunities, please open an issue on the dataset repository page.