init README.md

README.md

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+<a href="" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-thinking--in--space-red?logo=arxiv" height="20" />
+</a>
+<a href="" target="_blank">
+    <img alt="Website" src="https://img.shields.io/badge/🌎_Website-thinking--in--space-blue.svg" height="20" />
+</a>
+<a href="https://github.com/vision-x-nyu/thinking-in-space" target="_blank" style="display: inline-block; margin-right: 10px;">
+    <img alt="GitHub Code" src="https://img.shields.io/badge/Code-thinking--in--space-white?&logo=github&logoColor=white" />
+</a>
+
+
+# Visual Spatial Intelligence Benchmark (VSI-Bench)
+This repository contains the visual spatial intelligence benchmark (VSI-Bench), introduced in [Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces](https://arxiv.org/pdf/). 
+
+
+## Files
+The `test-00000-of-00001.parquet` contains the full dataset annotations and images pre-loaded for processing with HF Datasets. It can be loaded as follows:
+
+<!-- @shusheng -->
+```python
+from datasets import load_dataset
+vsi_bench = load_dataset("nyu-visionx/VSI-Bench")
+```
+
+Additionally, we provide the compressed raw videos in `*.zip`.
+
+
+## Dataset Description
+VSI-Bench quantitatively evaluate the visual-spatial intelligence of MLLMs from egocentric video. VSI-Bench comprises over 5,000 question-answer pairs derived from 288 real videos. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets `ScanNet`, `ScanNet++`, and `ARKitScenes` and represent diverse environments -- including residential spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g., factories) and multiple geographic regions. Repurposing these existing 3D reconstruction and understanding datasets offers accurate object-level annotations which we use in question generation and could enable future study into the connection between MLLMs and 3D reconstruction.
+
+The dataset contains the following fields:
+
+| Field Name | Description |
+| :--------- | :---------- |
+| `idx` | Global index of the entry in the dataset |
+| `dataset` | Video source: `scannet`, `arkitscenes` or `scannetpp` |
+| `question_type` | The type of task for question |
+| `question` | Question asked about the video |
+| `options` | Answer choices for the question (only for multiple choice questions) |
+| `ground_truth` | Correct answer to the question |
+| `video_suffix` | Suffix of the video  |
+
+
+<br>
+
+
+### Example Code
+
+<!-- @shusheng -->
+```python
+import pandas as pd
+# Load the CSV file into a DataFrame
+df = pd.read_csv('cv_bench_results.csv')
+# Define a function to calculate accuracy for a given source
+def calculate_accuracy(df, source):
+    source_df = df[df['source'] == source]
+    accuracy = source_df['result'].mean()  # Assuming 'result' is 1 for correct and 0 for incorrect
+    return accuracy
+# Calculate accuracy for each source
+accuracy_2d_ade = calculate_accuracy(df, 'ADE20K')
+accuracy_2d_coco = calculate_accuracy(df, 'COCO')
+accuracy_3d_omni = calculate_accuracy(df, 'Omni3D')
+# Calculate the accuracy for each type
+accuracy_2d = (accuracy_2d_ade + accuracy_2d_coco) / 2
+accuracy_3d = accuracy_3d_omni
+# Compute the combined accuracy as specified
+combined_accuracy = (accuracy_2d + accuracy_3d) / 2
+# Print the results
+print(f"CV-Bench Accuracy: {combined_accuracy:.4f}")
+print()
+print(f"Type Accuracies:")
+print(f"2D Accuracy: {accuracy_2d:.4f}")
+print(f"3D Accuracy: {accuracy_3d:.4f}")
+print()
+print(f"Source Accuracies:")
+print(f"ADE20K Accuracy: {accuracy_2d_ade:.4f}")
+print(f"COCO Accuracy: {accuracy_2d_coco:.4f}")
+print(f"Omni3D Accuracy: {accuracy_3d_omni:.4f}")
+```
+
+## Citation
+
+```bibtex
+@article{yang2024think,
+    title={{Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces}},
+    author={Yang, Jihan and Yang, Shusheng and Gupta, Anjali and Han, Rilyn and Fei-Fei, Li and Xie, Saining},
+    year={2024},
+    journal={arXiv preprint},
+}
+```