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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-to-image |
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- image-classification |
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- image-to-text |
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- image-to-3d |
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- image-segmentation |
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- image-to-video |
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- image-feature-extraction |
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tags: |
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- landscape |
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- wireframe |
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- synthetic-data |
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- computer-vision |
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- machine-learning |
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- training-data |
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- simulation |
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- neural-rendering |
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- autonomous-driving |
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- multimodal |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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<div style=" |
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background: #00FF00; |
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border-left: 4px solid #00FF00; |
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padding: 1.5rem; |
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margin: 2rem 0; |
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font-family: 'Fira Code', 'Courier New', monospace; |
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color: #00FF00; |
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border-radius: 0 8px 8px 0; |
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"> |
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<pre style=" |
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font-size: 12px; |
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line-height: 1.2; |
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margin: 0; |
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overflow-x: auto; |
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color: #00FF00; |
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"> |
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_ _ _ _______ ___________ _ _ ___________ _ ______ |
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| | | | \ | | _ \ ___| ___ \ | | || _ | ___ \ | | _ \ |
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| | | | \| | | | | |__ | |_/ / | | || | | | |_/ / | | | | | |
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| | | | . ` | | | | __|| /| |/\| || | | | /| | | | | | |
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| |_| | |\ | |/ /| |___| |\ \\ /\ /\ \_/ / |\ \| |___| |/ / |
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\___/\_| \_/___/ \____/\_| \_|\/ \/ \___/\_| \_\_____/___/ |
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</div> |
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## Underworld Dataset v2 |
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Generated with WEBXOS UNDERWORLD LANDSCAPE GENERATOR |
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**Download the GENERATOR from the /underworld/ folder of this repo to create your own datasets.** |
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Optimized for Hugging Face Overworld model training |
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### Dataset Details |
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- Frames: 120 |
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- Resolution: 1280x562 |
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- Objects: 150 |
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- Terrain Size: 160x160 |
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- View Mode: wireframe |
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### Usage for Training |
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1. Extract the dataset |
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2. Use frames/ folder for image sequences |
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3. Use metadata.json for generation parameters |
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4. Use labels.csv for object detection training |
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5. Use with Hugging Face transformers or custom training scripts |
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# WEBXOS Underworld Dataset v2 |
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WEBXOS Underworld Dataset v2 is a large-scale synthetic wireframe landscape dataset designed for training |
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and evaluating computer vision models, particularly for 3D reconstruction, depth estimation, and geometric |
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understanding tasks. Generated using a custom Three.js-based procedural generator, this dataset provides clean, |
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structured data with perfect ground truth annotations. |
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### Key Features: |
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- **Multi-modal Annotations**: RGB images, depth maps, surface normals, semantic segmentation, instance segmentation |
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- **Perfect Ground Truth**: No annotation noise, perfect correspondence across modalities |
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- **Procedural Diversity**: Parametrically generated landscapes with controlled variations |
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- **Camera Trajectories**: Multi-view sequences for 3D reconstruction and novel view synthesis |
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- **Wireframe Focus**: Emphasis on geometric structures and edges for edge detection and line segment detection tasks |
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### Primary Tasks: |
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- **Depth Estimation**: Predict depth from single RGB images |
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- **3D Reconstruction**: Reconstruct 3D scenes from single or multiple views |
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- **Semantic Segmentation**: Classify terrain and object categories |
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- **Instance Segmentation**: Identify individual object instances |
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- **Surface Normal Estimation**: Predict surface orientations |
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- **Edge Detection**: Detect wireframe edges and geometric boundaries |
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- **Novel View Synthesis**: Generate new views of 3D scenes |
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- **Object Detection**: Detect and localize objects in 3D space |
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### Data Fields |
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- `image`: RGB wireframe render (512×512 or 1024×1024) |
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- `depth`: Ground truth depth map (float32, normalized 0-1) |
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- `normal`: Surface normal map (3-channel float32) |
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- `segmentation`: Semantic segmentation mask (8 classes) |
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- `instances`: Instance segmentation mask |
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- `bounding_boxes`: 3D bounding boxes with categories |
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- `camera_params`: Camera intrinsics and extrinsics |
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- `terrain_params`: Procedural generation parameters |
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- `object_metadata`: Object positions, rotations, scales |
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### Data Splits |
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- `train`: 50,000 scenes (40,000 unique terrains + 10,000 augmentations) |
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- `validation`: 5,000 scenes |
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- `test`: 5,000 scenes (held-out for benchmarking) |
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### Citation |
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If you use this dataset, please cite: |
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WEBXOS UNDERWORLD Landscape Generator, 2026 |
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### License |
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cc-by-4.0 |
