This folder should contain CompenNeSt++ dataset. You can download and extract our dataset (~11G) here.
├─init # CompenNet and CompenNet++ initialization image, i.e., \dot{s} in Eq 13. Not used by CompenNeSt(++)
├─light1 # lighting level
│ ├─pos1 # cam, prj, surface pose
│ │ ├─cloud_np # surface texture, _np stands for nonplanar
│ │ │ ├─cam # camera-captured images or in images camera's view
│ │ │ │ ├─desire # desired effect, i.e., Fig. 1 (f)
│ │ │ │ │ └─test # testing images affine warped to displayable area, i.e., z' Fig. 5.
│ │ │ │ ├─raw # camera-captured raw images (w/o warping)
│ │ │ │ │ ├─ref # plain color images, ref/img_0126.png is \tilde(s)
│ │ │ │ │ ├─sl # SL images to convert raw->warpSL (two-step methods)
│ │ │ │ │ ├─test # CompenNet++ validation images \tilde(y)
│ │ │ │ │ └─train # CompenNet++ training images, \tilde(x)
│ │ │ │ └─warpSL # SL warped images, only used by two-step methods
│ │ │ │ ├─desire # SL warped cam/desire
│ │ │ │ ├─ref # SL warped raw/ref
│ │ │ │ ├─test # SL warped raw/test
│ │ │ │ └─train # SL warped raw/train
│ │ │ └─prj # compensation images (z*) of cam/desire/test (z') are saved here
│ . └─lavender_np # another setup with another surface texture
│ . .
│ . .
├─light2 # another lighting level
. .
. .
. .
├─ref # projector input plain color images, ref/img_gray.png is x0
├─sl # projector input SL images (see captured in cam/raw/sl)
├─test # projector input validation images, i.e., y
└─train # projector input training images, i.e., x
@article{huang2020CompenNeSt++,
title={End-to-end Full Projector Compensation},
author={Bingyao Huang and Tao Sun and Haibin Ling},
year={2021},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)} }
@inproceedings{huang2019CompenNeSt++,
author = {Huang, Bingyao and Ling, Haibin},
title = {CompenNeSt++: End-to-end Full Projector Compensation},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019} }
@inproceedings{huang2019compennet,
author = {Huang, Bingyao and Ling, Haibin},
title = {End-To-End Projector Photometric Compensation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019} }
We thank the anonymous reviewers for valuable and inspiring comments and suggestions. We thank the authors of the colorful textured sampling images.