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