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README.md

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+---
+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
+    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: 1769552
+    num_examples: 6158
+  download_size: 209977
+  dataset_size: 1769552
+- 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">
+
+  <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)
+
+  <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/xxxx.xxxx">
+    <img src="https://img.shields.io/badge/arXiv-xxxx.xxxx-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
+<TODO>
+```
+
+<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}
+}
+```

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