README.md

---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
tags:
- spatial-reasoning
- cross-viewpoint localization
pretty_name: ViewSpatial-Bench
size_categories:
- 1K<n<10K
configs:
- config_name: ViewSpatial-Bench
  data_files:
  - split: test
    path: ViewSpatial-Bench.json
---
# ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models

<!-- Provide a quick summary of the dataset. -->

## Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
We introduce ViewSpatial-Bench, a comprehensive benchmark with over 5,700 question-answer pairs across 1,000+ 3D scenes from ScanNet and MS-COCO validation sets. This benchmark evaluates VLMs' spatial localization capabilities from multiple perspectives, specifically testing both egocentric (camera) and allocentric (human subject) viewpoints across five distinct task types.
ViewSpatial-Bench addresses a critical gap: while VLMs excel at spatial reasoning from their own perspective, they struggle with perspective-taking—adopting another entity's spatial frame of reference—which is essential for embodied interaction and multi-agent collaboration.
- **Language(s) (NLP):** en
- **License:** apache-2.0

## Uses

**I. With HuggingFace datasets library.**
```py
from datasets import load_dataset
ds = load_dataset("lidingm/ViewSpatial-Bench")
```
**II. Evaluation using Open-Source Code.**
Evaluate using our open-source evaluation code available on Github.(Coming Soon)
```py
# Clone the repository
git clone https://github.com/lidingm/ViewSpatial-Bench.git
cd ViewSpatial-Bench

# Install dependencies
pip install -r requirements.txt

# Run evaluation
python eval.py --model_name your_model --dataset_path path/to/dataset
```
You can configure the appropriate model parameters and evaluation settings according to the framework's requirements to obtain performance evaluation results on the ViewSpatial-Bench dataset.

## Benchamrk
We provide benchmark results for various open-source models as well as **GPT-4o** and **Gemini 2.0 Flash** on our benchmark. *More model evaluations will be added.*
<table>
  <thead>
    <tr>
      <th rowspan="2">Model</th>
      <th colspan="3">Camera-based Tasks</th>
      <th colspan="4">Person-based Tasks</th>
      <th rowspan="2">Overall</th>
    </tr>
    <tr>
      <th>Rel. Dir.</th>
      <th>Obj. Ori.</th>
      <th>Avg.</th>
      <th>Obj. Ori.</th>
      <th>Rel. Dir.</th>
      <th>Sce. Sim.</th>
      <th>Avg.</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>InternVL2.5 (2B)</td>
      <td>38.52</td><td>22.59</td><td>32.79</td>
      <td>47.09</td><td>40.02</td><td>25.70</td><td>37.04</td>
      <td>34.98</td>
    </tr>
    <tr>
      <td>Qwen2.5-VL (3B) [Backbone]</td>
      <td>43.43</td><td>33.33</td><td>39.80</td>
      <td>39.16</td><td>28.62</td><td>28.51</td><td>32.14</td>
      <td>35.85</td>
    </tr>
    <tr>
      <td>Qwen2.5-VL (7B)</td>
      <td>46.64</td><td>29.72</td><td>40.56</td>
      <td>37.05</td><td>35.04</td><td>28.78</td><td>33.37</td>
      <td>36.85</td>
    </tr>
    <tr>
      <td>LLaVA-NeXT-Video (7B)</td>
      <td>26.34</td><td>19.28</td><td>23.80</td>
      <td>44.68</td><td>38.60</td><td>29.05</td><td>37.07</td>
      <td>30.64</td>
    </tr>
    <tr>
      <td>LLaVA-OneVision (7B)</td>
      <td>29.84</td><td>26.10</td><td>28.49</td>
      <td>22.39</td><td>31.00</td><td>26.88</td><td>26.54</td>
      <td>27.49</td>
    </tr>
    <tr>
      <td>InternVL2.5 (8B)</td>
      <td>49.41</td><td><b>41.27</b></td><td>46.48</td>
      <td>46.79</td><td>42.04</td><td><b>32.85</b></td><td>40.20</td>
      <td><b>43.24</b></td>
    </tr>
    <tr>
      <td>Llama-3.2-Vision (11B)</td>
      <td>25.27</td><td>20.98</td><td>23.73</td>
      <td>51.20</td><td>32.19</td><td>18.82</td><td>33.61</td>
      <td>28.82</td>
    </tr>
    <tr>
      <td>InternVL3 (14B)</td>
      <td><b>54.65</b></td><td>33.63</td><td><b>47.09</b></td>
      <td>33.43</td><td>37.05</td><td>31.86</td><td>33.88</td>
      <td>40.28</td>
    </tr>
    <tr>
      <td>Kimi-VL-Instruct (16B)</td>
      <td>26.85</td><td>22.09</td><td>25.14</td>
      <td><b>63.05</b></td><td><b>43.94</b></td><td>20.27</td><td><b>41.52</b></td>
      <td>33.58</td>
    </tr>
    <tr>
      <td>GPT-4o</td>
      <td>41.46</td><td>19.58</td><td>33.57</td>
      <td>42.97</td><td>40.86</td><td>26.79</td><td>36.29</td>
      <td>34.98</td>
    </tr>
    <tr>
      <td>Gemini 2.0 Flash</td>
      <td>45.29</td><td>12.95</td><td>33.66</td>
      <td>41.16</td><td>32.78</td><td>21.90</td><td>31.53</td>
      <td>32.56</td>
    </tr>
    <tr>
      <td>Random Baseline</td>
      <td>25.16</td><td>26.10</td><td>25.50</td>
      <td>24.60</td><td>31.12</td><td>26.33</td><td>27.12</td>
      <td>26.33</td>
    </tr>
  </tbody>
</table>



## Citation

```
Coming Soon
```