Update README.md

README.md

@@ -9,93 +9,91 @@ size_categories:
 - 10K<n<100K
 ---
 
-SQA3D: Situated Question Answering in 3D Scenes 
+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:
-```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."
-  }
+  ./assets/data/sqa_task/balanced/*
+  ./assets/data/sqa_task/answer_dict.json
   ```
-  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": 
+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
       {
-        "x": -0.9651003385573296,
-        "y": -1.2417634435553606,
-        "z": 0
-      },
-    "question_id": 220602000000,
-    "question_type": "N/A",
-    "rotation": 
+        "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
       {
-        "_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`.
+        "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'])
-```
+  ```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