OmniObject3D
Dataset
OmniObject3D is a large-scale 3D object dataset that includes over 6,000 high-quality real scanned 3D objects, covering 190 categories of everyday items, providing detailed mesh models, textures, and multi-view renderings, suitable for tasks such as 3D reconstruction, classification, and generation.
Dataset Highlights
A large-scale real scanned 3D object dataset that provides a solid foundation for 3D vision research
High-Quality Mesh Models
Each 3D object provides a detailed triangular mesh model with high polygon density and realistic geometric details, suitable for rendering and analysis.
Real 3D Scans
All objects are captured using professional 3D scanning equipment, not synthetically generated, ensuring the authenticity and accuracy of geometric shapes and surface textures.
190 Categories of Everyday Items
Covers 190 categories of everyday items such as furniture, food, toys, tools, and electronic devices, with a wide distribution of categories that closely resemble real-world scenes.
Multi-Angle Renderings
Provides multi-angle rendered images for each object, supporting research tasks such as Novel View Synthesis and multi-view 3D reconstruction.
Texture Maps
Includes high-resolution texture maps that realistically reproduce the surface materials, colors, and details of objects, suitable for realistic rendering and visual research.
Point Cloud Data
Provides high-density point cloud data sampled from mesh models, which can be directly used for point cloud classification, segmentation, and 3D feature learning algorithms.
Applicable Scenarios
From academic research to industrial applications, covering core tasks of 3D vision
3D Reconstruction
Utilize multi-view images and real geometric data to train and evaluate reconstruction algorithms such as NeRF and 3D Gaussian Splatting
Object Classification
Perform 3D object classification based on point clouds or mesh models, evaluating the performance of classification networks like PointNet and DGCNN
3D Generation
Provide high-quality training data for 3D diffusion models and generative adversarial networks, researching the generation of 3D objects from text/images
Novel View Synthesis
Train view synthesis models using multi-view rendered images to generate realistic images from sparse views at any angle
Data Preview
The following is a typical directory structure and metadata example of the OmniObject3D dataset
OmniObject3D/ ├── raw_scans/ │ ├── chair/ │ │ ├── chair_001/ │ │ │ ├── mesh.obj # Triangle mesh model │ │ │ ├── mesh.mtl # Material file │ │ │ ├── texture.png # Texture map │ │ │ └── pointcloud.ply # Point cloud data │ │ ├── chair_002/ │ │ └── ... │ ├── cup/ │ ├── shoe/ │ └── ... (190 categories) ├── renders/ │ ├── chair/ │ │ ├── chair_001/ │ │ │ ├── view_000.png # Multi-view rendered image │ │ │ ├── view_001.png │ │ │ └── ... │ │ └── ... │ └── ... └── metadata.json # Category and object metadata
3 Steps to Get Started Quickly
From browsing to loading, you can start your 3D visual research in just a few minutes
Browse Datasets
View dataset details on the Ace Data Cloud platform to understand metadata such as category distribution, object count, and data format.
Download Data
Download specific categories or the complete dataset as needed, including mesh models, texture maps, point cloud data, and multi-view renderings.
Load and Analyze
Use Open3D or trimesh to load 3D models and start research tasks such as reconstruction, classification, and generation.
Start Exploring OmniObject3D Data
A large-scale real scanned 3D object dataset with open licensing, available for immediate download. Whether you are a 3D vision researcher or a 3D generation developer, this dataset is worth trying.