Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| 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"> | |
| <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 | |
| <sup>2</sup> Hong Kong University of Science and Technology | |
| <br> | |
| <sup>3</sup> University of Waterloo | |
| <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} | |
| } | |
| ``` |