HorizonNet

This is the implementation of our CVPR'19 " HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation" (project page).

News, June 15, 2019 - Critical bug fix for general layout (dataset.py, inference.py and misc/post_proc.py)
News, Aug 19, 2019 - Report results on Structured3D dataset. (See the report 📋 on ST3D).

This repo is a pure python implementation that you can:

Inference on your images to get cuboid or general shaped room layout
3D layout viewer
Correct pose for your panorama images
Pano Stretch Augmentation copy and paste to apply on your own task
Quantitative evaluatation (3D IoU, Corner Error, Pixel Error)
- cuboid shape
- general shape
Your own dataset preparation and training

Method Pipeline overview:

Requirements

Python 3
pytorch>=1.0.0
numpy
scipy
sklearn
Pillow
tqdm
tensorboardX
opencv-python>=3.1 (for pre-processing) (also can't be too new, the latest opencv removed a key algorithm due to patent, 3.1.0.5 works. )
open3d>=0.7 (for layout 3D viewer)
shapely
torchvision

Download

Dataset

PanoContext/Stanford2D3D Dataset
- Download preprocessed pano/s2d3d for training/validation/testing
  - Put all of them under data directory so you should get:
```
HorizonNet/
├──data/
|  ├──layoutnet_dataset/
|  |  |--finetune_general/
|  |  |--test/
|  |  |--train/
|  |  |--valid/
```
  - test, train, valid are processed from LayoutNet's cuboid dataset.
  - finetune_general is re-annotated by us from train and valid. It contains 65 general shaped rooms.
Structured3D Dataset
- See the tutorial to prepare training/validation/testing for HorizonNet.

Pretrained Models

resnet50_rnn__panos2d3d.pth
- Trained on PanoContext/Stanford2d3d 817 pano images.
- Trained for 300 epoch
resnet50_rnn__st3d.pth
- Trained on Structured3D 18362 pano images with setting of original furniture and lighting.
- Trained for 50 epoch.
- Select 50th epoch according to loss function on validation set.

Inference on your images

In below explaination, I will use assets/demo.png for example.

(modified from PanoContext dataset)

1. Pre-processing (Align camera rotation pose)

Execution: Pre-process the above assets/demo.png by firing below command.
```
python preprocess.py --img_glob assets/demo.png --output_dir assets/preprocessed/
```
- --img_glob telling the path to your 360 room image(s).
  - support shell-style wildcards with quote (e.g. "my_fasinated_img_dir/*png").
- --output_dir telling the path to the directory for dumping the results.
- See python preprocess.py -h for more detailed script usage help.
Outputs: Under the given --output_dir, you will get results like below and prefix with source image basename.
- The aligned rgb images [SOURCE BASENAME]_aligned_rgb.png and line segments images [SOURCE BASENAME]_aligned_line.png
  - demo_aligned_rgb.png demo_aligned_line.png
- The detected vanishing points [SOURCE BASENAME]_VP.txt (Here demo_VP.txt)
```
-0.002278 -0.500449 0.865763
0.000895 0.865764 0.500452
0.999999 -0.001137 0.000178
```

2. Estimating layout with HorizonNet

Execution: Predict the layout from above aligned image and line segments by firing below command.
```
python inference.py --pth ckpt/resnet50_rnn__mp3d.pth --img_glob assets/preprocessed/demo_aligned_rgb.png --output_dir assets/inferenced --visualize
```
- --pth path to the trained model.
- --img_glob path to the preprocessed image.
- --output_dir path to the directory to dump results.
- --visualize optinoal for visualizing model raw outputs.
- --force_cuboid add this option if you want to estimate cuboid layout (4 walls).

Outputs: You will get results like below and prefix with source image basename.

The 1d representation are visualized under file name [SOURCE BASENAME].raw.png

The extracted corners of the layout [SOURCE BASENAME].json

{"z0": 50.0, "z1": -59.03114700317383, "uv": [[0.029913906008005142, 0.2996523082256317], [0.029913906008005142, 0.7240479588508606], [0.015625, 0.3819984495639801], [0.015625, 0.6348703503608704], [0.056027885526418686, 0.3881891965866089], [0.056027885526418686, 0.6278984546661377], [0.4480381906032562, 0.3970482349395752], [0.4480381906032562, 0.6178648471832275], [0.5995567440986633, 0.41122356057167053], [0.5995567440986633, 0.601679801940918], [0.8094607591629028, 0.36505699157714844], [0.8094607591629028, 0.6537724137306213], [0.8815288543701172, 0.2661873996257782], [0.8815288543701172, 0.7582473754882812], [0.9189453125, 0.31678876280784607], [0.9189453125, 0.7060701847076416]]}

3. Layout 3D Viewer

Execution: Visualizing the predicted layout in 3D using points cloud.
```
python layout_viewer.py --img assets/preprocessed/demo_aligned_rgb.png --layout assets/inferenced/demo_aligned_rgb.json --ignore_ceiling
```
- --img path to preprocessed image
- --layout path to the json output from inference.py
- --ignore_ceiling prevent showing ceiling
- See python layout_viewer.py -h for usage help.
Outputs: In the window, you can use mouse and scroll wheel to change the viewport

Your own dataset

See tutorial on how to prepare it.

Training

To train on a dataset, see python train.py -h for detailed options explaination.
Example:

python train.py --id resnet50_rnn

Important arguments:
- --id required. experiment id to name checkpoints and logs
- --ckpt folder to output checkpoints (default: ./ckpt)
- --logs folder to logging (default: ./logs)
- --pth finetune mode if given. path to load saved checkpoint.
- --backbone backbone of the network (default: resnet50)
  - other options: {resnet18,resnet34,resnet50,resnet101,resnet152,resnext50_32x4d,resnext101_32x8d,densenet121,densenet169,densenet161,densenet201}
- --no_rnn whether to remove rnn (default: False)
- --train_root_dir root directory to training dataset. (default: data/layoutnet_dataset/train)
- --valid_root_dir root directory to validation dataset. (default: data/layoutnet_dataset/valid/)
  - If giveng, the epoch with best 3DIoU on validation set will be saved as {ckpt}/{id}/best_valid.pth
- --batch_size_train training mini-batch size (default: 4)
- --epochs epochs to train (default: 300)
- --lr learning rate (default: 0.0001)

Quantitative Evaluation - Cuboid Layout

To evaluate on PanoContext/Stanford2d3d dataset, first running the cuboid trained model for all testing images:

python inference.py --pth ckpt/resnet50_rnn__panos2d3d.pth --img_glob "data/layoutnet_dataset/test/img/*" --output_dir output/panos2d3d/resnet50_rnn/ --force_cuboid

--img_glob shell-style wildcards for all testing images.
--output_dir path to the directory to dump results.
--force_cuboid enfoce output cuboid layout (4 walls) or the PE and CE can't be evaluated.

To get the quantitative result:

python eval_cuboid.py --dt_glob "output/panos2d3d/resnet50_rnn/*json" --gt_glob "data/layoutnet_dataset/test/label_cor/*txt"

--dt_glob shell-style wildcards for all the model estimation.
--gt_glob shell-style wildcards for all the ground truth.

If you want to:

just evaluate PanoContext only python eval_cuboid.py --dt_glob "output/panos2d3d/resnet50_rnn/*json" --gt_glob "data/layoutnet_dataset/test/label_cor/pano*txt"
just evaluate Stanford2d3d only python eval_cuboid.py --dt_glob "output/panos2d3d/resnet50_rnn/*json" --gt_glob "data/layoutnet_dataset/test/label_cor/camera*txt"

📋 The quantitative result for the released resnet50_rnn__panos2d3d.pth is shown below:

Testing Dataset	3D IoU(%)	Corner error(%)	Pixel error(%)
PanoContext	`83.39`	`0.76`	`2.13`
Stanford2D3D	`84.09`	`0.63`	`2.06`
All	`83.87`	`0.67`	`2.08`

Quantitative Evaluation - General Layout

See the report 📋 on ST3D for more detail.
See the report 📋 on MP3D for more detail.

TODO

Faster pre-processing script (top-fron alignment) (maybe cython implementation or fernandez2018layouts)

Acknowledgement

Credit of this repo is shared with ChiWeiHsiao.
Thanks limchaos for the suggestion about the potential boost by fixing the non-expected behaviour of Pytorch dataloader. (See Issue#4)

Citation

Please cite our paper for any purpose of usage.

@inproceedings{SunHSC19,
  author    = {Cheng Sun and
               Chi{-}Wei Hsiao and
               Min Sun and
               Hwann{-}Tzong Chen},
  title     = {HorizonNet: Learning Room Layout With 1D Representation and Pano Stretch
               Data Augmentation},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
               2019, Long Beach, CA, USA, June 16-20, 2019},
  pages     = {1047--1056},
  year      = {2019},
}

jbyu / HorizonNet