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.Grounded_3D_LLM_with_Referent_Tokens_Dataset
Grounded 3D-LLM Dataset
For detailed information and resources, please visit the following links:
Paper
Arxiv
Project Website
Dataset Access
Code
We are in the process of releasing our data incrementally:
Processed ScanNet200 PCD(~7G):
Each .npyfile represents a N*12 array with the following structure:
coordinates, color, normals, segments, labels = (
points[:, :3],
points[:, 3:6],
points[:, 6:9],
points[:, 9]… See the full description on the dataset page: https://huggingface.co/datasets/ShuaiYang03/Grounded_3D_LLM_with_Referent_Tokens_Dataset.2d_3d_seq_path_spatial_reasoning
Spatial Reasoning Dataset
A synthetic dataset of Hamiltonian path puzzles with rich chain-of-thought reasoning, designed for training and evaluating spatial reasoning in language models.
Overview
Each sample presents a grid-based puzzle where the solver must find a path visiting every cell exactly once, moving only up/down/left/right (plus above/below for 3D). Puzzles span 2D grids (3x3 to 8x8) and 3D cubes (3x3x3 to 4x4x4), covering solvable, impossible, and multi-turn… See the full description on the dataset page: https://huggingface.co/datasets/eousphoros/2d_3d_seq_path_spatial_reasoning.3D-Object-Conflictmy-distiset-3d6680f8
Dataset Card for my-distiset-3d6680f8
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/Bruno2023/my-distiset-3d6680f8/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Bruno2023/my-distiset-3d6680f8.3dx-users-guide-generated-errors-samplemultilingual-crossmodal-conflict-3D_Objects
Multilingual Cross-Modal Conflict — 3D Objects
A multilingual counterfactual MCQ dataset built from rendered 3D object scenes.
Each row contains a rendered 3D scene image, two captions (original vs counterfactual), and a multiple-choice question probing whether a VLM follows the image or the misleading text.
Languages
Language
Code
Rows
English
en
150
Hindi
hi
150
Telugu
te
150
Bahasa Indonesia
id
150
Columns
Column
Type… See the full description on the dataset page: https://huggingface.co/datasets/apart-global-south-hack/multilingual-crossmodal-conflict-3D_Objects.my-distiset-3d1aa117
Dataset Card for my-distiset-3d1aa117
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/trendfollower/my-distiset-3d1aa117/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/trendfollower/my-distiset-3d1aa117.