SQA3D / README.md
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metadata
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 under assets/data/. The following files should be used:
./assets/data/sqa_task/balanced/*
./assets/data/sqa_task/answer_dict.json
  1. 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:

    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:

    {
      "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:

    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

    {
      "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.

  1. We provide the mapping between answers and class labels in answer_dict.json
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'])
  1. Loader, model and training code can be found at https://github.com/SilongYong/SQA3D