Structured-light-stereo

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

STEP 1: Setup the stereo camera and calibrate it with a Charuco marker. 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.

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