Initial release: Real-3DQA v1.0 benchmark data + point clouds

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+  - question-answering
+  - visual-question-answering
+language:
+  - en
+tags:
+  - 3D
+  - spatial-reasoning
+  - point-cloud
+  - ScanNet
+  - SQA3D
+  - benchmark
+  - debiasing
+  - viewpoint-rotation
+size_categories:
+  - 1K<n<10K
+pretty_name: Real-3DQA
+---
+
+# Real-3DQA
+
+**Do 3D Large Language Models Really Understand 3D Spatial Relationships?**
+
+🌐 [Project Page](https://real-3dqa.github.io/) · 📄 [Paper (ICLR 2026)](https://openreview.net/forum?id=3vlMiJwo8b) · 💻 [GitHub](https://github.com/Real-3DQA/Real-3DQA.github.io)
+
+## Overview
+
+Real-3DQA is a debiased 3D spatial QA benchmark with **viewpoint rotation consistency** evaluation. It addresses two key shortcomings of existing benchmarks:
+
+1. **Language Shortcut Filtering** — Questions answerable through linguistic priors alone are removed by comparing 3D-LLMs against blind text-only counterparts.
+2. **Viewpoint Rotation Score (VRS)** — Each question is augmented with 4 viewpoints (0°, 90°, 180°, 270°) to test whether models produce consistent answers regardless of the observer's orientation.
+
+## Dataset Structure
+
+```
+data/
+├── rotation_0.json      # 750 questions at original (0°) viewpoint
+├── rotation_90.json     # same questions rotated 90° counter-clockwise
+├── rotation_180.json    # same questions rotated 180°
+└── rotation_270.json    # same questions rotated 270° counter-clockwise
+
+point_clouds/
+├── scene0025_00.pth     # Pre-processed ScanNet point clouds (33 scenes)
+├── scene0046_00.pth
+└── ...
+```
+
+## Data Format
+
+Each JSON file contains 750 questions. Each question has the following fields:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `question_id` | string | Unique identifier. Rotated versions append `01`/`02`/`03` suffix |
+| `scene_id` | string | ScanNet scene identifier (e.g., `scene0300_00`) |
+| `question_type` | string | One of: `distance`, `direction`, `counting`, `existence` |
+| `situation` | string | Natural language description of the observer's position and orientation |
+| `question` | string | The spatial question |
+| `answer` | string | Ground-truth answer |
+| `position` | object | Observer position `{x, y, z}` in ScanNet coordinates (z=0, ground plane) |
+| `rotation` | object | Observer orientation as quaternion `{_x, _y, _z, _w}` (rotation around Z-axis) |
+
+### Example
+
+```json
+{
+  "question_id": "220602000035",
+  "scene_id": "scene0300_00",
+  "question_type": "distance",
+  "situation": "I am facing the divider with the bookshelf on my right side.",
+  "question": "What is the first object closest to my left?",
+  "answer": "window",
+  "position": {"x": 1.032, "y": 0.734, "z": 0},
+  "rotation": {"_x": 0, "_y": 0, "_z": 0.996, "_w": 0.087}
+}
+```
+
+## Question Type Distribution
+
+| Type | Count | Description |
+|------|-------|-------------|
+| Distance | 594 | "What is closest to my left?" |
+| Direction | 113 | "In which direction is X relative to me?" |
+| Counting | 30 | "How many X are behind me?" |
+| Existence | 13 | "Is there a X on my right?" |
+
+## Point Clouds
+
+The `point_clouds/` directory contains 33 pre-processed ScanNet v2 point clouds in `.pth` format (from the [SQA3D](https://sqa3d.github.io/) processing pipeline). These cover all scenes referenced in the benchmark.
+
+## Evaluation
+
+**Exact Match (EM):** Standard QA accuracy at each rotation angle.
+
+**Viewpoint Rotation Score (VRS):** A question is scored as correct only if the model answers **all 4 rotations** correctly. This measures genuine 3D understanding rather than lucky guesses.
+
+## Contributing
+
+We welcome community contributions! If you find any data issues (incorrect answers, mismatched situations, etc.):
+
+1. Open a **Pull Request** via the "Community" tab
+2. In the PR description, specify the `question_id`, what's wrong, and the proposed correction
+3. We will review and merge valid corrections
+
+## Citation
+
+```bibtex
+@inproceedings{ma2026real3dqa,
+  title={Do 3D Large Language Models Really Understand 3D Spatial Relationships?},
+  author={Xianzheng Ma and Tao Sun and Shuai Chen and Yash Bhalgat and Jindong Gu and Angel X Chang and Iro Armeni and Iro Laina and Songyou Peng and Victor Adrian Prisacariu},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=3vlMiJwo8b}
+}
+```
+
+## License
+
+This dataset is released under the [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

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