Update Readme

README.md

@@ -54,3 +54,157 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# BlockGen-3D Dataset
+
+## Overview
+
+BlockGen-3D is a large-scale dataset of voxelized 3D models with accompanying text descriptions, specifically designed for text-to-3D generation tasks. By processing and voxelizing models from the [Objaverse dataset](https://huggingface.co/datasets/allenai/objaverse), we have created a standardized representation that is particularly suitable for training 3D generative models.
+
+Our dataset provides two types of representations: shape-only models represented as binary occupancy grids, and colored models with full RGBA information. Each model is represented in a 32×32×32 voxel grid, striking a balance between detail preservation and computational efficiency. This uniform representation makes the dataset especially suitable for training diffusion models and other deep learning architectures for 3D generation.
+
+The dataset contains 542,292 total samples, with:
+- 515,177 training samples
+- 27,115 test samples
+
+## Data Format and Structure
+
+Every sample in our dataset consists of a voxel grid with accompanying metadata. The voxel grid uses a consistent 32³ resolution, with values structured as follows:
+
+For shape-only data:
+- `voxels_occupancy`: A binary grid of shape [1, 32, 32, 32] where each voxel is either empty (0) or occupied (1)
+
+For colored data:
+- `voxels_colors`: RGB color information with shape [3, 32, 32, 32], normalized to [0, 1]
+- `voxels_occupancy`: The occupancy mask as described above
+
+Each sample also includes rich metadata:
+- Text descriptions derived from the original Objaverse dataset
+- Categorization and tagging information
+- Augmentation status and original file information
+- Number of occupied voxels for quick filtering or analysis
+
+## Using the Dataset
+
+### Basic Loading and Inspection
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("PeterAM4/blockgen-3d")
+
+# Access splits
+train_dataset = dataset["train"]
+test_dataset = dataset["test"]
+
+# Examine a sample
+sample = train_dataset[0]
+print(f"Sample description: {sample['name']}")
+print(f"Number of occupied voxels: {sample['metadata']['num_occupied']}")
+```
+
+### Working with Voxel Data
+
+When working with the voxel data, you'll often want to handle both shape-only and colored samples uniformly. Here's a utility function that helps standardize the processing:
+
+```python
+import torch
+
+def process_voxel_data(sample, default_color=[0.5, 0.5, 0.5]):
+    """Process voxel data with default colors for shape-only data.
+    
+    Args:
+        sample: Dataset sample
+        default_color: Default RGB values for shape-only models (default: gray)
+    
+    Returns:
+        torch.Tensor: RGBA data with shape [4, 32, 32, 32]
+    """
+    occupancy = torch.from_numpy(sample['voxels_occupancy'])
+    
+    if sample['voxels_colors'] is not None:
+        # Use provided colors for RGBA samples
+        colors = torch.from_numpy(sample['voxels_colors'])
+    else:
+        # Apply default color to shape-only samples
+        default_color = torch.tensor(default_color)[:, None, None, None]
+        colors = default_color.repeat(1, 32, 32, 32) * occupancy
+    
+    # Combine into RGBA format
+    rgba = torch.cat([colors, occupancy], dim=0)
+    return rgba
+```
+
+### Batch Processing with DataLoader
+
+For training deep learning models, you'll likely want to use PyTorch's DataLoader. Here's how to set it up with a simple prompt strategy:
+
+For basic text-to-3D generation, you can use the model names as prompts:
+```python
+def collate_fn(batch):
+    """Simple collate function using basic prompts."""
+    return {
+        'voxels': torch.stack([process_voxel_data(x) for x in batch]),
+        'prompt': [x['name'] for x in batch]  # Using name field as prompt, could also use more complex prompts from categories and tags dict keys
+    }
+
+# Create DataLoader
+dataloader = DataLoader(
+    train_dataset,
+    batch_size=32,
+    shuffle=True,
+    collate_fn=collate_fn
+)
+```
+
+## Visualization Examples
+
+
+
+## Dataset Creation Process
+
+Our dataset was created through the following steps:
+
+1. Starting with the Objaverse dataset
+2. Voxelizing 3D models at 32³ resolution
+3. Preserving color information where available
+4. Generating data augmentations through rotations
+5. Creating train/test splits (95%/5% split ratio)
+
+## Training tips
+
+- For shape-only models, use only the occupancy channel
+- For colored models:
+
+  - Apply colors only to occupied voxels
+  - Use default colors for shape-only samples
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+@misc{blockgen2024,
+  title={BlockGen-3D: A Large-Scale Dataset for Text-to-3D Voxel Generation},
+  author={Peter A. Massih},
+  year={2024},
+  publisher={Hugging Face}
+}
+
+Please also cite the original Objaverse dataset:
+
+@article{objaverse2023,
+  title={Objaverse: A Universe of Annotated 3D Objects},
+  author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and Oscar Michel and Eli VanderBilt and Ludwig Schmidt and Kiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},
+  journal={arXiv preprint arXiv:2304.02643},
+  year={2023}
+}
+
+## Limitations
+
+- Fixed 32³ resolution limits fine detail representation
+- Not all models have color information
+- Augmentations are limited to rotations
+- Voxelization may lose some geometric details
+
+