The premier large-vocabulary 3D benchmark for high-fidelity object reconstruction and generative AI.
OmniObject3D is a foundational large-scale vocabulary 3D object dataset and benchmarking suite designed to bridge the gap between synthetic 3D data and real-world high-quality captures. Architecturally, it encompasses over 6,000 scanned 3D objects spanning 190 categories, each meticulously captured via high-resolution professional-grade scanners. By 2026, OmniObject3D has established itself as the industry standard for evaluating 3D foundation models, particularly in the realms of NeRF (Neural Radiance Fields), 3D Gaussian Splatting, and 3D Diffusion. The dataset provides multi-modal representations including textured meshes, point clouds, and high-definition multi-view images with calibrated camera parameters. Its technical significance lies in its 'real-world' complexity—featuring diverse materials, intricate geometries, and realistic lighting environments that challenge current SOTA algorithms. For AI architects, OmniObject3D serves as the essential validation ground for robotic perception systems, AR/VR asset generation pipelines, and category-level pose estimation models, ensuring that generative outputs remain grounded in physical reality rather than synthetic artifacts.
Each object is scanned using professional hardware, resulting in dense geometry (50k+ faces) and high-fidelity 4K textures.
Real-time neural rendering and 3D reconstruction in seconds using multi-resolution hash encoding.
Physically-based shading integration for high-fidelity 3D Gaussian Splatting and reflective indoor scene reconstruction.
Segment and Edit Anything in 3D Scenes with Identity-Aware Gaussian Splatting
High-fidelity neural surface reconstruction for turning 2D video into detailed 3D digital twins.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Includes unified evaluation metrics and data splits for 3D reconstruction and novel view synthesis.
Provides data in multi-view images, point clouds, and mesh formats for every single object.
Features objects with varying BRDF properties, including specular, translucent, and matte surfaces.
Precisely calibrated camera poses for 100+ views per object, including lighting environment maps.
190+ categories mapped to WordNet hierarchy for semantic-aware 3D understanding.
Compatible with PyTorch3D and Mitsuba for gradient-based optimization of 3D shapes.
Lack of high-quality real-world 3D data for training 3D diffusion models.
Registry Updated:2/7/2026
Robots failing to interact with real objects due to synthetic-to-real gap.
Creating photorealistic 3D assets for mobile AR experiences.