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The dense depth map of a chessboard without laser

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3 comments

  • MartyG

    Hi Yixun Liu  Chessboards are a pattern that is easily recognizable to RealSense cameras as they commonly use that pattern for camera calibration.  An example of this is the RealSense Dynamic Calibration print target image.

    https://dev.intelrealsense.com/docs/dynamic-calibration-print-target

     

    Another example is the chessboard image used by the Python example program box_dimensioner_multicam.py to calibrate together the positions of multiple cameras placed around the chessboard.

    https://github.com/IntelRealSense/librealsense/blob/master/wrappers/python/examples/box_dimensioner_multicam/pattern_chessboard.png

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  • Yixun Liu

    Hi MartyG I know the chessboard is a regular calibration pattern. But from 3D reconstruction point of view the chessboard is not a good object because it has repeated pattern and lacks texture in most regions except the corners. But the depth map is dense and looks good. Given left and right infrared images I think the correspondence only can be found at the corners so the depth map should be very sparse. Is the dense map produced by some post processing?

     

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  • MartyX Grover

    The RealSense Viewer has a range of post-processing filters enabled by default. So you could test your theory by left-clicking on the blue icon beside Post-processing in the Viewer's options side-panel to disable all filters and see what effect it has on the depth map.

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