objaverse

Objaverse aggregates 800K+ 3D models from diverse sources (Sketchfab, TurboSquid, etc.) into a unified, searchable dataset with standardized metadata, canonical naming, and hierarchical object categorization. The dataset uses a multi-source ingestion pipeline that normalizes heterogeneous 3D formats (GLB, OBJ, USD) into a common representation, applies deduplication via perceptual hashing and geometric similarity metrics, and indexes objects by semantic category, license, and source provenance for efficient retrieval and filtering.

Objaverse indexes all 800K models with multi-level semantic categories (e.g., furniture → chair → office chair) derived from source metadata and automated tagging. Users can filter and retrieve subsets by category, enabling efficient dataset slicing without downloading the full corpus. The retrieval system supports both exact category matching and hierarchical traversal, allowing queries like 'all furniture' or 'all chairs' to return relevant subsets with consistent filtering semantics across heterogeneous source taxonomies.

Objaverse tracks license metadata for all 800K models (CC-BY, CC-0, proprietary, etc.) and enables filtering by license type and commercial-use permissions. The system maintains a license registry that maps source-specific license strings to standardized SPDX identifiers, allowing users to query 'all CC-BY models' or 'all models with commercial-use rights' without manual license verification. This enables compliant dataset construction for commercial applications and research with clear legal provenance.

Objaverse applies perceptual hashing, geometric similarity metrics, and metadata cross-referencing to identify and deduplicate models that appear across multiple sources (e.g., same model uploaded to both Sketchfab and TurboSquid). The system assigns canonical identifiers and names to deduplicated model groups, tracks source provenance for each variant, and enables users to retrieve all variants of a model or filter to a single canonical version. This prevents training data contamination and ensures fair representation across sources.

Objaverse stores all 800K models in standardized GLB (glTF binary) format with normalized geometry, materials, and metadata, enabling consistent programmatic access regardless of source format (OBJ, FBX, USD, etc.). The system provides APIs to load models as mesh tensors, extract geometry (vertices, faces, normals), access material properties (textures, PBR parameters), and query bounding boxes and scale information. This abstraction eliminates format-specific parsing and enables downstream systems to work with a uniform 3D representation.

Objaverse enables synthetic training data generation by providing APIs to render models with configurable camera angles, lighting, backgrounds, and material variations. The system supports batch rendering of multiple models with randomized parameters, enabling efficient generation of large synthetic datasets for 3D vision tasks (object detection, pose estimation, etc.). Rendering can be integrated with external engines (Blender, PyRender, etc.) or used with built-in lightweight rendering for rapid iteration.

Objaverse provides semantic search capabilities that enable users to find models by natural language queries (e.g., 'red wooden chair') or by geometric similarity to a reference model. The system uses pre-computed embeddings (semantic and geometric) to enable fast similarity search across the 800K model corpus. Users can query by category, text description, or by uploading a reference 3D model to find similar objects, enabling efficient dataset exploration and model discovery.