This repo uses a stereo camera and gray-code-based structured light to realize dense 3D reconstruction.

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• STEP 0: pip install opencv-python==4.5.4.60 opencv-contrib-python==4.5.4.60 (Version is necessary since the APIs are changed in higher versions.)

• STEP 1: Setup the stereo camera and calibrate it with a Charuco board. An example dataset is provided in ./data/stereo_calib_flir. You can play with it by running ./capture/charuco_calib.py. The calibration results will be saved in './data/stereo_calib_flir/stereo_calib_data.h5.

• STEP 2: Connect your projector with your computer and run gray_code_encoder.py to project gray-code patterns. I used two FLIR cameras to build my stereo system and the driver script is ./capture_calib/double_flir_capture.py. An example dataset is in ./data/bag.

• STEP 3: Run one of the three gray_code_decoder_*.py scripts to get the reconstructed point clouds. Differences among them are:

  • gray_code_decoder_disp.py: First recover a disparity map and rely on it to construct the point clouds. So the matching is column-direction only. This script can be the primary choice with good stereo calibration.
  • gray_code_decoder_tria_all_pts.py: First respectively construct the pixel-level camera-projector correspondences. Then triangulate the left-right camera pixels that correspond to the same projector's pixel. So the matching is 2D.
  • gray_code_decoder_tria_unique_pts.py: Same as above. But only pixels with unique correspondences are triangulated.

This repo is part of our DP-simulator project. Please consider to cite our paper if you find this repo useful

@inproceedings{li2023learning,
  title={Learning to Synthesize Photorealistic Dual-pixel Images from RGBD frames},
  author={Li, Feiran and Guo, Heng and Santo, Hiroaki and Okura, Fumio and Matsushita, Yasuyuki},
  booktitle={2023 IEEE International Conference on Computational Photography (ICCP)},
  pages={1--11},
  year={2023},
  organization={IEEE}
}