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Home/Tasks/ShapeNet
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ShapeNet

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Quick Tool Decision

Should you use ShapeNet?

ShapeNet is a richly-annotated, large-scale dataset of 3D shapes designed to enable research in computer graphics, computer vision, robotics, and related disciplines.

Category

Data & ML

Data confidence: release and verification fields are source-audited when available; other summary fields are community-aggregated.

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Overview

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.

Common tasks

Providing a large-scale dataset of 3D shapes for researchEnabling the development of 3D object recognition algorithmsSupporting research in 3D reconstruction and modelingFacilitating the creation of AI models for roboticsProviding annotated data for training machine learning modelsOffering a standardized benchmark for evaluating 3D shape analysis techniquesSupporting research in computer graphics and visualization

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