webxos/underworld_dataset_v2

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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.

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.

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

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)

License

cc-by-4.0