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---
license: cc-by-4.0
task_categories:
- question-answering
tags:
- 3D vision
- embodied AI
size_categories:
- 10K<n<100K
---
SQA3D: Situated Question Answering in 3D Scenes (ICLR 2023, https://arxiv.org/abs/2210.07474)
===
1. Download the [SQA3D dataset](https://zenodo.org/record/7544818/files/sqa_task.zip?download=1) under `assets/data/`. The following files should be used:
```
./assets/data/sqa_task/balanced/*
./assets/data/sqa_task/answer_dict.json
```
2. The dataset has been splited into `train`, `val` and `test`. For each category, we offer both question file, ex. `v1_balanced_questions_train_scannetv2.json`, and annotations, ex. `v1_balanced_sqa_annotations_train_scannetv2.json`
- The format of question file:
Run the following code:
```python
import json
q = json.load(open('v1_balanced_questions_train_scannetv2.json', 'r'))
# Print the total number of questions
print('#questions: ', len(q['questions']))
print(q['questions'][0])
```
The output is:
```json
{
"alternative_situation":
[
"I stand looking out of the window in thought and a radiator is right in front of me.",
"I am looking outside through the window behind the desk."
],
"question": "What color is the desk to my right?",
"question_id": 220602000000,
"scene_id": "scene0380_00",
"situation": "I am facing a window and there is a desk on my right and a chair behind me."
}
```
The following fileds are **useful**: `question`, `question_id`, `scene_id`, `situation`.
- The format of annotations:
Run the following code:
```python
import json
a = json.load(open('v1_balanced_sqa_annotations_train_scannetv2.json', 'r'))
# Print the total number of annotations, should be the same as questions
print('#annotations: ', len(a['annotations']))
print(a['annotations'][0])
```
The output is
```json
{
"answer_type": "other",
"answers":
[
{
"answer": "brown",
"answer_confidence": "yes",
"answer_id": 1
}
],
"position":
{
"x": -0.9651003385573296,
"y": -1.2417634435553606,
"z": 0
},
"question_id": 220602000000,
"question_type": "N/A",
"rotation":
{
"_w": 0.9950041652780182,
"_x": 0,
"_y": 0,
"_z": 0.09983341664682724
},
"scene_id": "scene0380_00"
}
```
The following fields are **useful**: `answers[0]['answer']`, `question_id`, `scene_id`.
**Note**: To find the answer of a question in the question file, you need to use lookup with `question_id`.
3. We provide the mapping between answers and class labels in `answer_dict.json`
```python
import json
j = json.load(open('answer_dict.json', 'r'))
print('Total classes: ', len(j[0]))
print('The class label of answer \'table\' is: ', j[0]['table'])
print('The corresponding answer of class 123 is: ', j[1]['123'])
```
4. Loader, model and training code can be found at https://github.com/SilongYong/SQA3D |