Datasets:
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license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- image-text-to-text
language:
- en
tags:
- spatial-intelligence
- geometric-reasoning
- benchmark
---
# PureSpace: A Benchmark for Abstract Spatial Reasoning in Vision-Language Models
[[Code](https://github.com/canglanx/purespace)]   [[Paper](https://openaccess.thecvf.com/content/CVPR2026F/papers/Li_PureSpace_A_Benchmark_for_Abstract_Spatial_Reasoning_in_Vision-Language_Models_CVPRF_2026_paper.pdf)]   [[Supp](https://openaccess.thecvf.com/content/CVPR2026F/supplemental/Li_PureSpace_A_Benchmark_CVPRF_2026_supplemental.pdf)]
<img src="assets/examples.jpg" alt="Examples" width="50%">
## Dataset Structure
```text
purespace/
├── images/
│ ├── l3_c221/
│ │ ├── 000009/
│ │ │ ├── 000009_iso.jpg
│ │ │ ├── 000009_top.jpg
│ │ │ └── ...
│ │ └── ...
│ └── ...
│
└── labels/
├── rotation/
│ ├── train/
│ │ ├── l3_c221.txt
│ │ └── ...
│ └── test/
│ ├── l3_c221.txt
│ └── ...
├── projection/
│ └── ...
└── completion/
└── ...
```
## Usage Example
```python
import os
import glob
# Define dataset directory
data_root = "/path/to/purespace"
# Question texts
q_texts = {
"rotation": "Which option is a rotation of the given object?",
"projection": "Which option is a top-down view of the given object?",
"completion": "Which option fits the given object, in order to make a cube?",
}
# All label files
label_files = sorted(glob.glob(os.path.join(data_root, "labels", "*", "*", "*.txt")))
# Read each label file
for label_file in label_files:
with open(label_file, "r") as f:
label_lines = [line.strip().split() for line in f.readlines()]
# Read each line in label file
for line in label_lines:
# Question image
q_img = os.path.join(data_root, "images", line[0], line[3])
# Hard option images
hard_o_imgs = [
os.path.join(data_root, "images", line[0], rel_path)
for rel_path in line[4].split(",")
]
# Hard answer idx
hard_a = int(line[5])
# Easy option images
easy_o_imgs = [
os.path.join(data_root, "images", line[0], rel_path)
for rel_path in line[6].split(",")
]
# Easy answer idx
easy_a = int(line[7])
# Metadata
metadata = os.path.join(
data_root, "images", line[0], line[1], f"{line[1]}_metadata.json"
)
print(f"\n--- Dataset Sample Preview ---")
print(f"{'Setting Name:':<16}{line[0]}")
print(f"{'Object ID:':<16}{line[1]}")
print(f"{'Task Type:':<16}{line[2]}")
print(f"\nQuestion Image:")
print(f" {q_img}")
print(f"\nQuestion Text:")
print(f" {q_texts[line[2]]}")
print(f"\nHard Option Images ({len(hard_o_imgs)}):")
for img in hard_o_imgs:
print(f" {img}")
print(f"{'Hard Answer Index:':<20}{hard_a}")
print(f"\nEasy Option Images ({len(easy_o_imgs)}):")
for img in easy_o_imgs:
print(f" {img}")
print(f"{'Easy Answer Index:':<20}{easy_a}")
print(f"\nMetadata:")
print(f" {metadata}")
break
break
```
## Citation
```
@inproceedings{li2026purespace,
title = {PureSpace: A Benchmark for Abstract Spatial Reasoning in Vision-Language Models},
author = {Li, Jinkai and Zhang, Zhenliang and Fan, Lifeng and Wang, Wei},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings},
year = {2026},
}
```
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