---
license: mit
pretty_name: WorldRenderer Dataset Test
task_categories:
  - image-to-image
  - text-to-image
  - depth-estimation
language:
  - en
tags:
  - 3d
  - neural-rendering
  - novel-view-synthesis
  - multiview
  - synthetic-data
  - depth
  - normal-map
  - world-model
  - diffusion
  - rendering
size_categories:
  - 10K<n<100K
viewer: false
---


# 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


| Property | Value |
|---|---|
| Number of scenes | 27 |
| Frames per scene | 401 |
| Camera trajectory | Single monocular trajectory |
| Modalities | RGB / Depth / Normal |
| Geometry assets | Included |
| Rendering type | Synthetic |
| Alignment | Pixel-aligned multi-modal |


---


# 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:


```text
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
```


⸻

Naming Convention

All frame indices start from 0.

Frame naming format:

rgb_000000.png
depth_000000.png
normal_000000.png

The final frame index is:

000400

corresponding to a total of 401 frames per scene.

⸻

Data Fields

RGB

Rendered RGB images along the camera trajectory.

Example:

rgb/rgb_000123.png

⸻

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

⸻

Normal

Surface normal maps aligned with RGB images.

Example:

normal/normal_000123.png

Normals are represented in camera space.

⸻

Model

Original textured 3D scene asset.

model/

⸻

Data Generation Pipeline

For each scene:

1. A textured 3D environment is loaded.
2. A monocular camera trajectory is generated.
3. 401 aligned frames are rendered.
4. RGB, depth, and normal maps are exported.
5. All assets are organized into scene-wise folders.

⸻

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

⸻

Example Usage

Basic File Access

from pathlib import Path
root = Path("worldrenderer-dataset-test")
scene_dir = root / "0000"
rgb_path = scene_dir / "rgb" / "rgb_000000.png"
depth_path = scene_dir / "depth" / "depth_000000.png"
normal_path = scene_dir / "normal" / "normal_000000.png"
print(rgb_path)

⸻

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

⸻

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

⸻

Contact

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