datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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 (VRS) — Each… See the full description on the dataset page: https://huggingface.co/datasets/Oliver-Ma/Real-3DQA.3DQA
3D Question Answering ScanNet Export
This dataset card publishes a Hugging Face Dataset Viewer-ready JSONL export for the
3D Question Answering project by Shuquan Ye,
Dongdong Chen, Songfang Han, and Jing Liao.
Related links:
Project page
arXiv
Code
Dataset repository
Splits
train: 9160 examples from scene0000_00 through scene0706_00; this intentionally combines the original train and validation portion. Because some cloud files were lost, the train and… See the full description on the dataset page: https://huggingface.co/datasets/pleaseconnectwifi/3DQA.SR-3D-Bench
Spatial Region 3D (SR-3D) Aware Benchmark
Paper: https://arxiv.org/abs/2509.13317Project page: https://www.anjiecheng.me/sr3dCode: https://github.com/AnjieCheng/SR-3D
[!IMPORTANT]
[Feb. 18, 2026] UPDATE: To improve compatibility with general-purpose VLMs, the benchmark is reformulated into multiple-choice and numerical questions following the VSI-Bench evaluation protocol. Videos are annotated with set-of-marks to explicitly indicate regions. The benchmark will be compatible… See the full description on the dataset page: https://huggingface.co/datasets/a8cheng/SR-3D-Bench.3dx-users-guide-generated-errors-sample