--- language: - en license: apache-2.0 size_categories: - 10K Spatial457 Logo

Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models

Xingrui Wang¹, Wufei Ma¹, Tiezheng Zhang¹, Celso M. de Melo², Jieneng Chen¹, Alan Yuille¹

¹ Johns Hopkins University ² DEVCOM Army Research Laboratory

🌐 Project Page • 📄 Paper • 🤗 Dataset • 💻 Code

Spatial457 Teaser

--- ## 🧠 Introduction **Spatial457** is a diagnostic benchmark designed to evaluate **6D spatial reasoning** in large multimodal models (LMMs). It systematically introduces four core spatial capabilities: - 🧱 Multi-object understanding - 🧭 2D spatial localization - 📦 3D spatial localization - 🔄 3D orientation estimation These are assessed across **five difficulty levels** and **seven diverse question types**, ranging from simple object queries to complex reasoning about physical interactions. --- ## 📂 Dataset Structure The dataset is organized as follows: ``` Spatial457/ ├── images/ # RGB images used in VQA tasks ├── questions/ # JSONs for each subtask │ ├── L1_single.json │ ├── L2_objects.json │ ├── L3_2d_spatial.json │ ├── L4_occ.json │ └── ... ├── Spatial457.py # Hugging Face dataset loader script ├── README.md # Documentation ``` Each JSON file contains a list of VQA examples, where each item includes: - "image_filename": image file name used in the question - "question": natural language question - "answer": boolean, string, or number - "program": symbolic program (optional) - "question_index": unique identifier This modular structure supports scalable multi-task evaluation across levels and reasoning types. --- ## 🛠️ Dataset Usage You can load the dataset directly using the Hugging Face 🤗 `datasets` library: ### 🔹 Load a specific subtask (e.g., L5_6d_spatial) ```python from datasets import load_dataset dataset = load_dataset("RyanWW/Spatial457", name="L5_6d_spatial", split="validation", data_dir=".") ``` Each example is a dictionary like: ```python { 'image': , 'image_filename': 'superCLEVR_new_000001.png', 'question': 'Is the large red object in front of the yellow car?', 'answer': 'True', 'program': [...], 'question_index': 100001 } ``` ### 🔹 Other available configurations ```python [ "L1_single", "L2_objects", "L3_2d_spatial", "L4_occ", "L4_pose", "L5_6d_spatial", "L5_collision" ] ``` You can swap `name="..."` in `load_dataset(...)` to evaluate different spatial reasoning capabilities. ## 📊 Benchmark We benchmarked a wide range of state-of-the-art models—including GPT-4o, Gemini, Claude, and several open-source LMMs—across all subsets. The results below have been updated after rerunning the evaluation. While they show minor variance compared to the results in the published paper, the conclusions remain unchanged. The inference script supports [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) and is run by setting the dataset to `Spatial457`. You can find the detailed inference scripts [here](https://github.com/XingruiWang/VLMEvalKit). ### Spatial457 Evaluation Results | Model | L1_single | L2_objects | L3_2d_spatial | L4_occ | L4_pose | L5_6d_spatial | L5_collision | |------------------------|-----------|------------|---------------|--------|---------|----------------|---------------| | **GPT-4o** | 72.39 | 64.54 | 58.04 | 48.87 | 43.62 | 43.06 | 44.54 | | **GeminiPro-1.5** | 69.40 | 66.73 | 55.12 | 51.41 | 44.50 | 43.11 | 44.73 | | **Claude 3.5 Sonnet** | 61.04 | 59.20 | 55.20 | 40.49 | 41.38 | 38.81 | 46.27 | | **Qwen2-VL-7B-Instruct** | 62.84 | 58.90 | 53.73 | 26.85 | 26.83 | 36.20 | 34.84 | --- ## 📚 Citation ```bibtex @inproceedings{wang2025spatial457, title = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models}, author = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2025}, url = {https://arxiv.org/abs/2502.08636} } ```