README.md

---
dataset_info:
- config_name: 16_frame
  features:
  - name: id
    dtype: int64
  - name: dataset
    dtype: string
  - name: scene_id
    dtype: string
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: ground_truth
    dtype: string
  - name: options
    sequence: string
  - name: num_frames
    dtype: string
  - name: queried_object_ids
    sequence: int64
  splits:
  - name: test
    num_bytes: 1211030
    num_examples: 4568
  download_size: 147029
  dataset_size: 1211030
- config_name: 32_frame
  features:
  - name: id
    dtype: int64
  - name: dataset
    dtype: string
  - name: scene_id
    dtype: string
  - name: question_type
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  - name: question
    dtype: string
  - name: ground_truth
    dtype: string
  - name: options
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  - name: num_frames
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  - name: queried_object_ids
    sequence: int64
  splits:
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- config_name: 64_frame
  features:
  - name: id
    dtype: int64
  - name: dataset
    dtype: string
  - name: scene_id
    dtype: string
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: ground_truth
    dtype: string
  - name: options
    sequence: string
  - name: num_frames
    dtype: string
  - name: queried_object_ids
    sequence: int64
  splits:
  - name: test
    num_bytes: 1931345
    num_examples: 6616
  download_size: 231397
  dataset_size: 1931345
- config_name: all_frame
  features:
  - name: id
    dtype: int64
  - name: dataset
    dtype: string
  - name: scene_id
    dtype: string
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: ground_truth
    dtype: string
  - name: options
    sequence: string
  - name: num_frames
    dtype: string
  - name: queried_object_ids
    sequence: int64
  splits:
  - name: test
    num_bytes: 2010779
    num_examples: 6808
  download_size: 239453
  dataset_size: 2010779
configs:
- config_name: 16_frame
  data_files:
  - split: test
    path: 16_frame/test-*
- config_name: 32_frame
  data_files:
  - split: test
    path: 32_frame/test-*
- config_name: 64_frame
  data_files:
  - split: test
    path: 64_frame/test-*
- config_name: all_frame
  data_files:
  - split: test
    path: all_frame/test-*
  default: true
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
license: apache-2.0
tags:
- Spatial Intelligence
- Vision Language Models
---

<div align="center">
  <img src="metadata/revsi.png" width="350">
  <b>ICML 2026</b>
  <br>
  <a href="https://github.com/eamonn-zh">Yiming Zhang</a><sup>1*</sup>,
  <a href="https://jcchen.me/">Jiacheng Chen</a><sup>1*</sup>,
  <a href="https://christinatan0704.github.io/mysite/">Jiaqi Tan</a><sup>1</sup>,
  <a href="https://sammaoys.github.io/">Yongsen Mao</a><sup>2</sup>,
  <a href="https://wenhuchen.github.io/">Wenhu Chen</a><sup>3</sup>,
  <a href="https://angelxuanchang.github.io/">Angel X. Chang</a><sup>1,4</sup>
  <br>
  <sup>1</sup> Simon Fraser University &nbsp;&nbsp;
  <sup>2</sup> Hong Kong University of Science and Technology
  <br>
  <sup>3</sup> University of Waterloo &nbsp;&nbsp;
  <sup>4</sup> Alberta Machine Intelligence Institute (Amii)
  <br><br>
  <a href="https://3dlg-hcvc.github.io/revsi/">
    <img src="https://img.shields.io/badge/Project%20Page-84C0B8?style=for-the-badge">
  </a>
  <a href="https://github.com/3dlg-hcvc/revsi">
    <img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white">
  </a>
  <a href="https://arxiv.org/abs/2604.24300">
    <img src="https://img.shields.io/badge/arXiv-2604.24300-b31b1b.svg?style=for-the-badge">
  </a>
  <a href="https://revsi.site/">
    <img src="https://img.shields.io/badge/Visualizer-84C0B8?style=for-the-badge&logo=eye&logoColor=white">
  </a>
</div>

This repository contains the <span style="color:#84C0B8;"><b>ReVSI</b></span> benchmark and dataset, introduced in [ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning](https://3dlg-hcvc.github.io/revsi/).


## Data Subsets
<span style="color:#84C0B8;"><b>ReVSI</b></span> provides multiple data subsets corresponding to different video frame budgets:
- all-frame
- 64-frame
- 32-frame
- 16-frame

Use the following command to load a specific subset:
```python
from datasets import load_dataset
revsi_dataset = load_dataset("3dlg-hcvc/ReVSI", "64_frame", split="test")  # load the 64-frame subset
```

> [!NOTE]
> **How video subsets are constructed:**
> 
> The **all-frame** subset contains the full processed video sequence for each scene, with standardized resolution and frame rate:
> 1. **ScanNet v2 / ScanNetPP v2 / MultiScan**  
>    *640 × 480 · 10 FPS*
>
> 2. **ARKitScenes**  
>    *640 × 480 / 480 × 640 · 10 FPS (all videos have been rotated to sky-up orientation)*
>
> 3. **3RScan**  
>    *360 × 640 · 4 FPS*
> 
> The fixed-budget subsets are constructed via hierarchical uniform sampling:
> 1. Uniformly sample **64 frames** from **all-frame**
> 2. Uniformly subsample **32 frames** from the **64-frame** set
> 3. Uniformly subsample **16 frames** from the **32-frame** set  
>
> This produces a nested structure: **16-frame** ⊂ **32-frame** ⊂ **64-frame** ⊂ **all-frame**.
> For each video, all subsets cover the same time span, and each sampled frame keeps the same timestamp across subsets. This guarantees consistent timestamps for models with frame timestamp encoding.

## Data Fields
Each entry in <span style="color:#84C0B8;"><b>ReVSI</b></span> dataset contains the following fields:
| Field Name | Type | Description |
| :--------- | :--- | :---------- |
| `id` | int64 | Unique identifier for each sample |
| `dataset` | string | Source dataset of the video |
| `scene_id` | string | Identifier of the scene (video) associated with the sample |
| `question_type` | string | Category of the question |
| `question` | string | Natural language question grounded in the video |
| `options` | list[string] | List of answer choices (only for multiple-choice questions) |
| `ground_truth` | string | Ground-truth answer to the question |
| `num_frames` | string | Frame budget used for evaluation (e.g., 16, 32, 64, all) |
| `queried_object_ids` | list[int64] | List of object instance IDs referenced in the question |

## Evaluation
> [!WARNING]
> Please avoid using PyTorch 2.9, as a known cuDNN issue can lead to significant performance degradation for QwenVL models (see [details](https://github.com/pytorch/pytorch/issues/166122)).

<span style="color:#84C0B8;"><b>ReVSI</b></span> supports inference / evaluation with the following frameworks:
- [LMMs-Eval](https://github.com/eamonn-zh/lmms-eval) (inference + evaluation)
  ```bash
  # example 1: evaluate Qwen3-VL-8B-Instruct on ReVSI 64-frame subset (with huggingface transformers backend on 4 GPUs)
  accelerate launch \
  --num_processes=4 \
  -m lmms_eval \
  --model qwen3_vl \
  --model_args=pretrained=Qwen/Qwen3-VL-8B-Instruct,attn_implementation=flash_attention_2,max_num_frames=64 \
  --tasks revsi_64_frame \
  --batch_size 8
  
  # example 2: evaluate Qwen3-VL-8B-Instruct on ReVSI all-frame subset using 2 fps sampling rate (with vllm backend)
  python -m lmms_eval \
  --model vllm \
  --model_args "model=Qwen/Qwen3-VL-8B-Instruct,fps=2" \
  --tasks revsi_all_frame
  ```

- [VLMEvalKit](https://github.com/eamonn-zh/VLMEvalKit) (inference + evaluation)
  ```bash
  # example 1: evaluate Qwen3-VL-8B-Instruct on ReVSI 32-frame subset (with vllm backend)
  python run.py --data revsi_32_frame --model Qwen3-VL-8B-Instruct
  ```

- [ModelScope SWIFT](https://github.com/modelscope/ms-swift) (inference-only, check [ReVSI GitHub repo](https://github.com/3dlg-hcvc/revsi) for data registration)
  ```bash
  # example 1: infer Qwen3-VL-8B-Instruct on ReVSI 64-frame subset (with huggingface transformers backend on 4 GPUs)
  NPROC_PER_NODE=4 swift infer \
  --model Qwen/Qwen3-VL-8B-Instruct  \
  --model_kwargs '{"fps_min_frames": 64, "fps_max_frames": 64}' \
  --val_dataset 3dlg-hcvc/ReVSI:64_frame \
  --infer_backend transformers \
  --custom_register_path ./ms_swift_register/revsi_register.py \
  --use_hf true \
  --torch_dtype bfloat16 \
  --attn_impl flash_attention_2 \
  --strict true \
  --max_batch_size 8 \
  --temperature 0
  ```

- [TorchMetrics Extension](https://github.com/eamonn-zh/torchmetrics_ext) (evaluation-only)
  ```python
  # example 1: evaluate existing predictions on ReVSI all-frame subset using TorchMetrics Extension evaluator
  from torchmetrics_ext.metrics.vqa import ReVSIMetric

  metric = ReVSIMetric(subset=all_frame)
  predictions = {0: "2", 1: "4", ..., 1000: "A"}  # predictions should be a dict following the format {question_id: response}
  results = metric(pred_dict)
  ```

## Metadata Files
We provide several metadata files used in constructing <span style="color:#84C0B8;"><b>ReVSI</b></span>:

- [metadata/3d_annotation.json](https://huggingface.co/datasets/3dlg-hcvc/ReVSI/blob/main/metadata/3d_annotation.json): 3D annotations for each scene, including object names, oriented bounding boxes and scene area polygons. The schema is as follows:
  ```json
  [
    {
      "scene_id": # scene ID from the source dataset
      "dataset":  # source dataset name
      "scene_area_2d_polygon": # list of 2D boundary points (x, y) defining the scene area polygon, shape (N, 2)
      "scene_area_type":  # scene area annotation type (single_room or multiple_room)
      "objects": [
        {
          "id":  # object id within the scene
          "name":  # open-vocabulary object name
          "obb": {
            "center":  # center of the object oriented bounding boxes, shape (3, )
            "extent":  # extent of the object oriented bounding boxes, shape (3, )
            "rotation":  # rotation matrix of the object oriented bounding boxes, shape (3, 3)
          }
        },
        ...
      ]
    },
    ...
  ]
  ```

- [metadata/sampled_video_frame_idx.json](https://huggingface.co/datasets/3dlg-hcvc/ReVSI/blob/main/metadata/sampled_video_frame_idx.json): indices of sampled frames for the 16/32/64-frame subsets. The scehema is as follows:
  ```json
  {
    "<scene_id>": {
      "64-frame":  # list of sampled frame indices from the all-frame video, shape (64, )
      "32-frame":  # list of sampled frame indices from the all-frame video, shape (32, )
      "16-frame":  # list of sampled frame indices from the all-frame video, shape (16, )
    }
    ...
  }
  ```

- [metadata/obj_visibility.json](https://huggingface.co/datasets/3dlg-hcvc/ReVSI/blob/main/metadata/obj_visibility.json): Object visibility under different video frame budgets. The schema is as follows:
  ```json
  {
    "<scene_id>": [
      {
        "object_id":  # object id within the scene (consistent with metadata/3d_annotation.json)
        "object_name":  # open-vocabulary object name (consistent with metadata/3d_annotation.json)
        "visibility_16":  # visibility under the 16-frame budget
        "visibility_32":  # visibility under the 32-frame budget
        "visibility_64":  # visibility under the 64-frame budget
      },
      ...
    ],
    ...
  }
  ```

- [metadata/tiny_set_question_ids.txt](https://huggingface.co/datasets/3dlg-hcvc/ReVSI/blob/main/metadata/tiny_set_question_ids.txt): The sampled question ids of `tiny` set for proprietary model evaluations.

## Citation
If you find <span style="color:#84C0B8;"><b>ReVSI</b></span> useful for your research, please consider citing:
```bibtex
@article{zhang2026revsi,
  title={ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning},
  author={Zhang, Yiming and Chen, Jiacheng and Tan, Jiaqi and Mao, Yongsen and Chen, Wenhu and Chang, Angel X.},
  journal={arXiv preprint arXiv:2604.24300},
  year={2026}
}
```

<span style="color:#84C0B8;"><b>ReVSI</b></span> builds upon the following 3D scene datasets and the VSI-Bench benchmark, please also consider citing:
```bibtex
@inproceedings{dai2017scannet,
  title={Scannet: Richly-annotated 3d reconstructions of indoor scenes},
  author={Dai, Angela and Chang, Angel X and Savva, Manolis and Halber, Maciej and Funkhouser, Thomas and Nie{\ss}ner, Matthias},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5828--5839},
  year={2017}
}

@inproceedings{yeshwanth2023scannet++,
  title={Scannet++: A high-fidelity dataset of 3d indoor scenes},
  author={Yeshwanth, Chandan and Liu, Yueh-Cheng and Nie{\ss}ner, Matthias and Dai, Angela},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12--22},
  year={2023}
}

@inproceedings{baruch1arkitscenes,
  title={ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data},
  author={Baruch, Gilad and Chen, Zhuoyuan and Dehghan, Afshin and Feigin, Yuri and Fu, Peter and Gebauer, Thomas and Kurz, Daniel and Dimry, Tal and Joffe, Brandon and Schwartz, Arik and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)}
}

@inproceedings{wald2019rio,
  title={Rio: 3d object instance re-localization in changing indoor environments},
  author={Wald, Johanna and Avetisyan, Armen and Navab, Nassir and Tombari, Federico and Nie{\ss}ner, Matthias},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7658--7667},
  year={2019}
}

@article{mao2022multiscan,
  title={Multiscan: Scalable rgbd scanning for 3d environments with articulated objects},
  author={Mao, Yongsen and Zhang, Yiming and Jiang, Hanxiao and Chang, Angel and Savva, Manolis},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={9058--9071},
  year={2022}
}

@inproceedings{yang2025thinking,
  title={Thinking in space: How multimodal large language models see, remember, and recall spaces},
  author={Yang, Jihan and Yang, Shusheng and Gupta, Anjali W and Han, Rilyn and Fei-Fei, Li and Xie, Saining},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={10632--10643},
  year={2025}
}
```