Update README.md

README.md

@@ -1,3 +1,101 @@
 ---
 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 
+===
+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:
+```plain
+./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