---
license: cc-by-4.0
task_categories:
- image-to-image
- image-classification
- image-to-text
- image-to-3d
- image-segmentation
- image-to-video
- image-feature-extraction
tags:
- landscape
- wireframe
- synthetic-data
- computer-vision
- machine-learning
- training-data
- simulation
- neural-rendering
- autonomous-driving
- multimodal
---

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## Underworld Dataset v2

Generated with WEBXOS UNDERWORLD LANDSCAPE GENERATOR

**Download the GENERATOR from the /underworld/ folder of this repo to create your own datasets.**

Optimized for Hugging Face Overworld model training

### Dataset Details
- Frames: 120
- Resolution: 1280x562
- Objects: 150
- Terrain Size: 160x160
- View Mode: wireframe

### Usage for Training
1. Extract the dataset
2. Use frames/ folder for image sequences
3. Use metadata.json for generation parameters
4. Use labels.csv for object detection training
5. Use with Hugging Face transformers or custom training scripts


# WEBXOS Underworld Dataset v2

WEBXOS Underworld Dataset v2 is a large-scale synthetic wireframe landscape dataset designed for training 
and evaluating computer vision models, particularly for 3D reconstruction, depth estimation, and geometric 
understanding tasks. Generated using a custom Three.js-based procedural generator, this dataset provides clean, 
structured data with perfect ground truth annotations.

### Key Features:

- **Multi-modal Annotations**: RGB images, depth maps, surface normals, semantic segmentation, instance segmentation
- **Perfect Ground Truth**: No annotation noise, perfect correspondence across modalities
- **Procedural Diversity**: Parametrically generated landscapes with controlled variations
- **Camera Trajectories**: Multi-view sequences for 3D reconstruction and novel view synthesis
- **Wireframe Focus**: Emphasis on geometric structures and edges for edge detection and line segment detection tasks

### Primary Tasks:

- **Depth Estimation**: Predict depth from single RGB images
- **3D Reconstruction**: Reconstruct 3D scenes from single or multiple views
- **Semantic Segmentation**: Classify terrain and object categories
- **Instance Segmentation**: Identify individual object instances
- **Surface Normal Estimation**: Predict surface orientations
- **Edge Detection**: Detect wireframe edges and geometric boundaries
- **Novel View Synthesis**: Generate new views of 3D scenes
- **Object Detection**: Detect and localize objects in 3D space

### Data Fields

- `image`: RGB wireframe render (512×512 or 1024×1024)
- `depth`: Ground truth depth map (float32, normalized 0-1)
- `normal`: Surface normal map (3-channel float32)
- `segmentation`: Semantic segmentation mask (8 classes)
- `instances`: Instance segmentation mask
- `bounding_boxes`: 3D bounding boxes with categories
- `camera_params`: Camera intrinsics and extrinsics
- `terrain_params`: Procedural generation parameters
- `object_metadata`: Object positions, rotations, scales

### Data Splits

- `train`: 50,000 scenes (40,000 unique terrains + 10,000 augmentations)
- `validation`: 5,000 scenes
- `test`: 5,000 scenes (held-out for benchmarking)

### Citation

If you use this dataset, please cite:
WEBXOS UNDERWORLD Landscape Generator, 2026

### License
cc-by-4.0