--- 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

ICML 2026
Yiming Zhang^1*, Jiacheng Chen^1*, Jiaqi Tan¹, Yongsen Mao², Wenhu Chen³, Angel X. Chang^1,4
¹ Simon Fraser University ² Hong Kong University of Science and Technology
³ University of Waterloo ⁴ Alberta Machine Intelligence Institute (Amii)

This repository contains the ReVSI 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 ReVSI 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 ReVSI 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)). ReVSI 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 ReVSI: - [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 { "": { "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 { "": [ { "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 ReVSI 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} } ``` ReVSI 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} } ```