Cityscapes Dataset
Cityscapes is a large-scale dataset for semantic urban scene understanding, providing high-quality pixel-level annotations of street scenes from 50 different cities.
ShapeNet is a richly-annotated, large-scale dataset of 3D shapes designed to enable research in computer graphics, computer vision, robotics, and related disciplines.

ShapeNet is a collaborative research project by Princeton, Stanford, and TTIC that provides a comprehensive dataset of 3D shapes. It offers researchers access to annotated 3D models for use in various fields like computer graphics, computer vision, and robotics. The dataset includes ShapeNetCore, featuring single, clean 3D models with verified category and alignment annotations covering 55 common object categories. ShapeNetSem, a smaller but densely annotated subset, offers models with real-world dimensions, material composition estimates, and volume/weight estimates. ShapeNet aims to accelerate research by providing a standardized, large-scale resource for training and evaluating 3D shape analysis algorithms and models. Access to ShapeNet requires user registration, and the data is available for download.
ShapeNet is a collaborative research project by Princeton, Stanford, and TTIC that provides a comprehensive dataset of 3D shapes.
Explore all tools that specialize in providing a large-scale dataset of 3d shapes for research. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
Explore all tools that specialize in enabling the development of 3d object recognition algorithms. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
Explore all tools that specialize in supporting research in 3d reconstruction and modeling. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
Explore all tools that specialize in facilitating the creation of ai models for robotics. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
Explore all tools that specialize in providing annotated data for training machine learning models. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
Explore all tools that specialize in offering a standardized benchmark for evaluating 3d shape analysis techniques. This domain focus ensures ShapeNet delivers optimized results for this specific requirement.
A subset of ShapeNet with single, clean 3D models. It features manually verified category and alignment annotations and covers 55 common object categories.
A smaller, densely annotated subset of ShapeNet containing 12,000 models across 270 categories. It includes real-world dimensions, material composition estimates, and volume/weight estimates.
Allows users to explore the ShapeNet dataset through a hierarchical taxonomy of object categories, making it easy to find specific types of 3D models.
Enables users to search for 3D models based on keywords, categories, and other criteria. The search functionality helps researchers quickly locate the models they need.
ShapeNetCore is now available for download via Hugging Face, providing seamless access to the dataset within the Hugging Face ecosystem.
Visit the ShapeNet website at https://shapenet.org/.
Create an account by clicking on "Sign In" and then "Create an Account".
Fill out the registration form with your information.
Verify your email address by clicking the link in the confirmation email.
Sign in to your ShapeNet account.
Browse the available datasets, such as ShapeNetCore and ShapeNetSem.
Download the desired datasets after agreeing to the terms of use.
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"ShapeNet is a valuable resource for researchers in computer vision, computer graphics, and robotics, providing a large, annotated dataset of 3D shapes. Its use has been demonstrated in numerous publications, indicating its importance and widespread adoption."
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Cityscapes is a large-scale dataset for semantic urban scene understanding, providing high-quality pixel-level annotations of street scenes from 50 different cities.
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