add data structure in readme

data/scannet/README.md

 ### Prepare ScanNet Data
 We follow the procedure in [votenet](https://github.com/facebookresearch/votenet/).
 
-1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Move/link the `scans` folder such that under `scans` there should be folders with names such as `scene0001_01`.
+1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Link or move the 'scans' folder to this level of directory.
 
-2. Extract point clouds and annotations (semantic seg, instance seg etc.) by running `python batch_load_scannet_data.py`, which will create a folder named `scannet_train_instance_data` here.
+2. In this level of directory, extract point clouds and annotations by running `python batch_load_scannet_data.py`.
 
 3. Enter the project root directory, generate training data by running `python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet`.
+
+```
+scannet
+├── scannet_utils.py
+├── load_scannet_data.py
+├── README.md
+├── scans
+
+```

data/sunrgbd/README.md

@@ -3,10 +3,29 @@ We follow the procedure in [votenet](https://github.com/facebookresearch/votenet
 
 1. Download SUNRGBD v2 data [HERE](http://rgbd.cs.princeton.edu/data/). Then, move SUNRGBD.zip, SUNRGBDMeta2DBB_v2.mat, SUNRGBDMeta3DBB_v2.mat and SUNRGBDtoolbox.zip to the OFFICIAL_SUNRGBD folder, unzip the zip files.
 
-2. Enter the `matlab` folder, run `extract_split.m`, `extract_rgbd_data_v2.m` and `extract_rgbd_data_v1.m` to extract point clouds and annotations.
+2. Enter the `matlab` folder, Extract point clouds and annotations by running `extract_split.m`, `extract_rgbd_data_v2.m` and `extract_rgbd_data_v1.m`.
 
-3. Back to this level, prepare data by running `python sunrgbd_data.py --gen_v1_data`
+3. Back to this level of directory, prepare data by running `python sunrgbd_data.py --gen_v1_data`.
+
+4. Enter the project root directory, Generate training data by running `python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd`.
 
-4. Enter the project root directory, generate training data by running `python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd`.
 
 NOTE: SUNRGBDtoolbox.zip should have MD5 hash `18d22e1761d36352f37232cba102f91f` (you can check the hash with `md5 SUNRGBDtoolbox.zip` on Mac OS or `md5sum SUNRGBDtoolbox.zip` on Linux)
+
+
+```
+sunrgbd
+├── sunrgbd_utils.py
+├── sunrgbd_data.py
+├── README.md
+├── matlab
+│   ├── extract_rgbd_data_v1.m
+│   ├── extract_rgbd_data_v2.m
+│   ├── extract_split.m
+├── OFFICIAL_SUNRGBD
+│   ├── SUNRGBD
+│   ├── SUNRGBDMeta2DBB_v2.mat
+│   ├── SUNRGBDMeta3DBB_v2.mat
+│   ├── SUNRGBDtoolbox
+
+```

data/sunrgbd/sunrgbd_data.py

@@ -103,9 +103,10 @@ def extract_sunrgbd_data(idx_filename,
                          save_votes=False,
                          use_v1=False,
                          skip_empty_scene=True):
-    """ Extract scene point clouds and
-    bounding boxes (centroids, box sizes, heading angles, semantic classes).
-    Dumped point clouds and boxes are in upright depth coord.
+    """Extract scene point clouds and
+            bounding boxes (centroids, box sizes, heading angles,
+            semantic classes). Dumped point clouds and boxes are in
+            upright depth coord.
 
     Args:
         idx_filename: a TXT file where each line is an int number (index)