| --- |
| 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 |
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| WorldRenderer Dataset Test is a synthetic multi-scene 3D rendering dataset designed for research in: |
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|
| - 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: |
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| - RGB images |
| - Depth maps |
| - Surface normal maps |
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| Additionally, each scene also contains: |
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| - the original textured 3D scene asset |
| - the first rendered frame |
| - an automatically generated caption for the first frame |
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| 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 | |
|
|
|
|
| --- |
|
|
|
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| # Supported Tasks |
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|
|
| 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 |
|
|
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|
| 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 |
| ``` |
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| ⸻ |
|
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| Naming Convention |
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| All frame indices start from 0. |
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| Frame naming format: |
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|
| 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 |
|
|
| ⸻ |
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| Depth |
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| Depth maps aligned with RGB images. |
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| Example: |
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| 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 |
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|
| Normals are represented in camera space. |
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| ⸻ |
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| Model |
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| Original textured 3D scene asset. |
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| model/ |
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| ⸻ |
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| Data Generation Pipeline |
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| For each scene: |
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| 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. |
|
|
| ⸻ |
|
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| Intended Uses |
|
|
| The dataset is designed for research purposes, including: |
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| * training neural rendering systems |
| * studying geometry-aware generation |
| * evaluating multi-view consistency |
| * camera-conditioned generation |
| * 3D scene understanding |
| * synthetic world modeling |
|
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| Potential model families include: |
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| * NeRF-based methods |
| * Gaussian Splatting pipelines |
| * Diffusion Transformers |
| * Multi-view autoregressive models |
| * Geometry-aware diffusion models |
| * Vision-language world models |
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| ⸻ |
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| Example Usage |
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| 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. |
