End-to-end Projector Photometric Compensation (CVPR'19 Oral)

Introduction

PyTorch implementation of CompenNet. Also see CompenNet++ and journal version.

Highlights:

• For the first time, we formulate the compensation problem as an end-to-end learning problem and propose a convolutional neural network, named CompenNet, to implicitly learn the complex compensation function.
• A novel evaluation benchmark, which is independent of system setup and thus quantitatively verifiable. Such benchmark is not previously available.
• Our method is evaluated carefully on the benchmark, and the results show that our end-to-end learning solution outperforms state-of-the-arts both qualitatively and quantitatively by a significant margin.

For more info please refer to our CVPR'19 paper and supplementary material.

Prerequisites

• PyTorch compatible GPU
• Python 3
• PyTorch >= 0.4.0 (note that the results in the paper were produced using this version)
• opencv-python 3.4.4
• visdom (for visualization)

Usage

1. Clone this repo:

    git clone https://github.com/BingyaoHuang/CompenNet
    cd CompenNet
   

2. Install required packages by typing

    pip install -r requirements.txt
   

3. Download CompenNet benchmark dataset and extract to CompenNet/data

4. Start visdom by typing

    visdom
   

5. Once visdom is successfully started, visit http://localhost:8097

6. Open main.py and set which GPUs to use. An example is shown below, we use GPU 0, 2 and 3 to train the model.

    os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2, 3'
    device_ids = [0, 1, 2]
   

7. Run main.py to start training and testing

    cd src/python
    python main.py
   

8. The training and validation results are updated in the browser during training. An example is shown below, where the 1st figure shows the training and validation loss, rmse and ssim curves. The 2nd and 3rd montage figures are the training and validation pictures, respectively. In each montage figure, the 1st rows are the camera captured uncompensated images, the 2nd rows are CompenNet predicted projector input images and the 3rd rows are ground truth of projector input image.

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Apply CompenNet to your own setup

1. For a planar textured projection surface, adjust the camera-projector such that the brightest projected input image (plain white) slightly overexposes the camera captured image. Similarly, the darkest projected input image (plain black) slightly underexposes the camera captured image. This allows the projector dynamic range to cover the full camera dynamic range.
2. Project and capture the images in CompenNet/data/train and CompenNet/data/test. Note that in our setup, we stretched the image to cover the entire projection area (keep aspect ratio).
3. Project and capture a surface image CompenNet/data/ref/img_gray.
4. Project and capture a checkerboard image.
5. Estimate the homography H between camera and projector image, then warp the camera captured images train, test and img_gray to projector's view using H.
6. Finally save the warped images to CompenNet/data/light[n]/pos[m]/warp/[surface]/train, CompenNet/data/light[n]/pos[m]/[surface]/warp/test and CompenNet/data/light[n]/pos[m]/[surface]/warp/ref, respectively, where [n] and [m] are lighting setup index and position setup index, [surface] is projection surface's name.

Note ref/img_0001.png to ref/img_0125.png are plain color training images used by TPS-based method, you don't need to use these images to train CompenNet.

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Implement your own CompenNet model

1. Create a new class e.g., NewModel in CompenNetModel.py.
2. Add the new class name string to model_list, e.g., model_list = ['NewModel', 'CompenNet'] in main.py.
3. Run main.py.
4. The quantitative comparison results between NewModel and CompenNet will be saved to log/%Y-%m-%d_%H_%M_%S.txt and an example is shown below, where prefix uncmp_ means the similarity between uncompensated camera-captured images and ground truth of projector input images. Prefix valid_ means the similarity between CompenNet predicted projector input images and ground truth of projector input images. Refer to our CVPR'19 paper Fig. 3 for more details.

    data_name              model_name   loss_function   num_train  batch_size  max_iters  uncmp_psnr  uncmp_rmse  uncmp_ssim  valid_psnr  valid_rmse  valid_ssim     
    light2/pos1/curves     NewModel     l1+ssim         500        64          1000       14.3722     0.3311      0.5693      23.2068     0.1197      0.7974   
    light2/pos1/curves     CompenNet    l1+ssim         500        64          1000       14.3722     0.3311      0.5693      23.1205     0.1209      0.7943   
    light2/pos1/squares    NewModel     l1+ssim         500        64          1000       10.7664     0.5015      0.5137      20.1589     0.1701      0.7032  
    light2/pos1/squares    CompenNet    l1+ssim         500        64          1000       10.7664     0.5015      0.5137      20.1673     0.1699      0.7045   
    light1/pos1/stripes    NewModel     l1+ssim         500        64          1000       15.1421     0.3030      0.6264      24.9872     0.0975      0.8519 
    light1/pos1/stripes    CompenNet    l1+ssim         500        64          1000       15.1421     0.3030      0.6264      24.9245     0.0983      0.8508     
    light2/pos2/lavender   NewModel     l1+ssim         500        64          1000       13.1573     0.3808      0.5665      22.2718     0.1333      0.7723   
    light2/pos2/lavender   CompenNet    l1+ssim         500        64          1000       13.1573     0.3808      0.5665      22.1861     0.1347      0.7693 

More qualitative comparison results

Citation

@inproceedings{huang2019compennet,
    author = {Huang, Bingyao and Ling, Haibin},
    title = {End-To-End Projector Photometric Compensation},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2019}
}

Acknowledgments

The PyTorch implementation of SSIM loss is modified from Po-Hsun-Su/pytorch-ssim. We thank the anonymous reviewers for valuable and inspiring comments and suggestions. We thank the authors of the colorful textured sampling images.

License

This software is freely available for non-profit non-commercial use, and may be redistributed under the conditions in license.