Oliver-Ma/Real-3DQA
Real-3DQA Do 3D Large Language Models Really Understand 3D Spatial Relationships? 🌐 Project Page · 📄 Paper · 💻 GitHub Overview Real-3DQA is a debiased 3D spatial QA benchmark with viewpoint rotation consistency evaluation. It addresses two key shortcomings of existing benchmarks: Language Shortcut Filtering — Questions answerable through linguistic priors alone are removed by comparing 3D-LLMs against blind text-only counterparts. Viewpoint Rotation Score… See the full description on the dataset page: https://huggingface.co/datasets/Oliver-Ma/Real-3DQA.
| Author / organization | Oliver-Ma |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 2K (as reported by Hugging Face) |
| Likes | 8 |
| Created | 9 March 2026 |
| Last updated at source | 7 April 2026 |
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- 16 Oct 2026 Description: Real-3DQA Do 3D Large Language Models Really Understand 3D Spatial Relationships? 🌐 Project Page · 📄 Paper · 💻 GitHub Overview Real-3DQA is a debiased 3D spatial QA benchmark with viewpoint rotation consistency evaluation. It addresses t → Real-3DQA Do 3D Large Language Models Really Understand 3D Spatial Relationships? 🌐 Project Page · 📄 Paper · 💻 GitHub Overview Real-3DQA is a debiased 3D spatial QA benchmark with viewpoint rotation consistency evaluation. It addresses t
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