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 to quantitatively evaluate VLMs' spatial localization capabilities in 3D environments from multiple perspectives. Our benchmark contains over 5,700 question-answer pairs spanning more than 1,000 unique 3D scenes, with source imagery from the validation sets of ScanNet and MS-CoCo. 
ViewSpatial-Bench is the first comprehensive benchmark designed specifically for evaluating multi-viewpoint spatial orientation recognition capabilities of vision-language models (VLMs) across five distinct task types. The benchmark assesses how well VLMs can perform spatial reasoning from different perspectives, focusing on both egocentric (camera) and allocentric (human subject) viewpoints.
The benchmark addresses a critical limitation in current VLMs: while they excel at egocentric spatial reasoning (from the camera's perspective), they struggle to generalize to allocentric viewpoints when required to adopt another entity's spatial frame of reference. This capability, known as "perspective-taking," is crucial for embodied interaction, spatial navigation, 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
```