BlockGen-3D is a large-scale dataset of voxelized 3D models with accompanying text descriptions, designed for text-to-3D generation tasks. The dataset was created by author PeterAM4, who processed and voxelized models from the Objaverse dataset to create a standardized representation suitable for training 3D diffusion models. It was last updated on January 9,我们发现了一个错误。
Use Cases
- Training text-to-3D diffusion models based on the paired voxel models and text descriptions.
- Benchmarking 3D generation algorithms based on the standardized voxel representations.
- Researching multimodal learning for 3D shapes based on the shape-only and colored model variants.
Strengths
- Designed specifically for text-to-3D generation tasks, providing a targeted resource.
- Provides two types of representations: binary occupancy grids and colored models, offering flexibility.
- Based on the established Objaverse dataset, suggesting a foundation of diverse 3D objects.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for large-scale training.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Processed from the Objaverse dataset.
- Collection Method
- Voxelization of 3D models from Objaverse.
- Time Range
- null
- Freshness
- Last updated 2025-01-09 09:26:59; freshness should be verified.
- Geography
- null