change copyright, change README, add README in configs

configs/votenet/README.md

+# Deep Hough Voting for 3D Object Detection in Point Clouds
+## Introduction
+```
+@inproceedings{qi2019deep,
+    author = {Qi, Charles R and Litany, Or and He, Kaiming and Guibas, Leonidas J},
+    title = {Deep Hough Voting for 3D Object Detection in Point Clouds},
+    booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
+    year = {2019}
+}
+```
+
+## Results and models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |
+
+## Different regression loss
+|    Backbone     | Loss type | Mem (GB) | Inf time (fps) | box AP | Download |
+
+## Pre-trained Models
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |

data/scannet/README.md

@@ -3,10 +3,14 @@ We follow the procedure in [votenet](https://github.com/facebookresearch/votenet
 
 1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Link or move the 'scans' folder to this level of directory.
 
-2. In this level of directory, extract point clouds and annotations by running `python batch_load_scannet_data.py`.
+2. In this 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`.
+3. Enter the project root directory, generate training data by running
+```bash
+python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet
+```
 
+The directory structure after pre-processing should be as below
 ```
 scannet
 ├── scannet_utils.py

data/scannet/batch_load_scannet_data.py

 # Modified from
 # https://github.com/facebookresearch/votenet/blob/master/scannet/batch_load_scannet_data.py
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 """Batch mode in loading Scannet scenes with vertices and ground truth labels
 for semantic and instance segmentations
 
 Usage example: python ./batch_load_scannet_data.py
 """
+
 import argparse
 import datetime
 import os

data/scannet/load_scannet_data.py

 # Modified from
 # https://github.com/facebookresearch/votenet/blob/master/scannet/load_scannet_data.py
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 """Load Scannet scenes with vertices and ground truth labels
 for semantic and instance segmentations
 """
+
 import argparse
 import inspect
 import json

data/scannet/scannet_utils.py

 # Modified from
 # https://github.com/facebookresearch/votenet/blob/master/scannet/scannet_utils.py
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 """Ref: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts
 """
+
 import csv
 import os
 

data/sunrgbd/README.md

@@ -5,14 +5,17 @@ We follow the procedure in [votenet](https://github.com/facebookresearch/votenet
 
 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 of directory, prepare data by running `python sunrgbd_data.py --gen_v1_data`.
+3. Back to this 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
+```bash
+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)
 
-
+The directory structure after pre-processing should be as below
 ```
 sunrgbd
 ├── sunrgbd_utils.py

data/sunrgbd/matlab/extract_split.m

 % Modified from
 % https://github.com/facebookresearch/votenet/blob/master/sunrgbd/matlab/extract_split.m
-% Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+% Copyright (c) Facebook, Inc. and its affiliates.
+%
+% This source code is licensed under the MIT license found in the
+% LICENSE file in the root directory of this source tree.
 
 %% Dump train/val split.
 % Author: Charles R. Qi

data/sunrgbd/sunrgbd_data.py

 # Modified from
 # https://github.com/facebookresearch/votenet/blob/master/sunrgbd/sunrgbd_data.py
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 ''' Helper class and functions for loading SUN RGB-D objects
 
 Author: Charles R. Qi
@@ -10,6 +13,7 @@ Note: removed unused code for frustum preparation.
 Changed a way for data visualization (removed depdency on mayavi).
 Load depth with scipy.io
 '''
+
 import argparse
 import os
 import sys

data/sunrgbd/sunrgbd_utils.py

 # Modified from
 # https://github.com/facebookresearch/votenet/blob/master/sunrgbd/sunrgbd_utils.py
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 """Provides Python helper function to read My SUNRGBD dataset.
 
 Author: Charles R. Qi