--- dataset_info: features: - name: voxels_occupancy dtype: array4_d: shape: - 1 - 32 - 32 - 32 dtype: float32 - name: voxels_colors dtype: array4_d: shape: - 3 - 32 - 32 - 32 dtype: float32 - name: model_id dtype: string - name: name dtype: string - name: categories sequence: string - name: tags sequence: string - name: metadata struct: - name: is_augmented dtype: bool - name: original_file dtype: string - name: num_occupied dtype: int32 - name: split dtype: string splits: - name: train num_bytes: 207426028571 num_examples: 515177 - name: test num_bytes: 10942650293 num_examples: 27115 download_size: 4338398625 dataset_size: 218368678864 configs: - config_name: default data_files: - split: train path: data/train-* - 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 diffusion 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. 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 Usage Example](https://huggingface.co./datasets/PeterAM4/blockgen-3d/resolve/main/comparison_wooden.gif) ![Dataset Usage Example](https://huggingface.co./datasets/PeterAM4/blockgen-3d/resolve/main/comparison_char.gif) *Dataset Usage Example - Showing voxelization process* ## 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