complete readme

data/scannet/README.md

@@ -4,4 +4,4 @@
 
 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.
 
-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`
+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`.

data/sunrgbd/README.md

@@ -6,6 +6,6 @@
 
 3. Prepare data by running `python sunrgbd_data.py --gen_v1_data`
 
-You can also examine and visualize the data with `python sunrgbd_data.py --viz` and use MeshLab to view the generated PLY files at `data_viz_dump`.
+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)