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</style>
<div class="omniobj3d-page">
<section class="omniobj3d-hero">
<div class="s-container-narrow">
<div class="hero-badge">
<span class="badge-dot"></span>
OmniObject3D Dataset
</div>
<h1>
OmniObject3D<br/><span>Dataset</span>
</h1>
<p class="hero-subtitle">
OmniObject3D is a large-scale 3D object dataset that contains 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 comes with a detailed triangular mesh model, featuring high polygon density and real geometric details, ready 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 including furniture, food, toys, tools, electronics, etc., with a wide distribution of categories that closely resemble real-world scenarios.
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, directly usable for point cloud classification, segmentation, and 3D feature learning algorithms.
Applicable Scenarios
From academic research to industrial applications, covering core tasks in 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-angle rendered images to generate realistic images from sparse views at any angle.
Data Preview
Below is a typical directory structure and metadata example for the OmniObject3D dataset.
OmniObject3D/ ├── raw_scans/ │ ├── chair/ │ │ ├── chair_001/ │ │ │ ├── mesh.obj # Triangular 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-Angle 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 the Dataset
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.