Home / Research Library / ShapeNet: An Information-Rich 3D Model Repository
🤖 Artificial Intelligence OpenAlex

ShapeNet: An Information-Rich 3D Model Repository

📅 November 9, 2023 👤 Angel X. Chang, Thomas Funkhouser, Leonidas Guibas et al. 📖 Research Journal 📊 1,998 citations

🤖 Plain-English Summary

Authors listed alphabetically We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of ob-jects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the Word-Net taxonomy.

🔑 Key Findings

  • ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the Word-Net taxonomy.
  • It is a collection of datasets providing many semantic annotations for each 3D model such as consis-tent rigid alignments, parts and bilateral symmetry planes,

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

Read the full paper
Access the original peer-reviewed research via OpenAlex.

View on DOI ↗

📋 Article Details

Category 🤖 Artificial Intelligence
Published Nov 09, 2023
Journal Research Journal
Authors Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang
DOI 10.5281/zenodo.10089705
Citations 1,998
Source OpenAlex

More 🤖 Artificial Intelligence Research