MartyG-RealSense
commented
10 months ago Hi @OldLiu666 If your depth problem is primarily due to reflections then applying a physical optical filter prodct called a linear polarization filter over the lenses on the outside of the camera can significantly help to negate glare from reflections.
Any thin-film polarizing filter should work so long as it is linear, so they can be obtained inexpensively. You can search stores such as Amazon for the term for the term linear polarizing filter sheet to research example filter products.
Section 4.4 When to use polarizers and waveplates in Intel's white-paper guide to using optical filters with RealSense 400 Series cameras has more information about this type of filter.
The image below from that section demonstrates the before and after effects of a linear polarization filter.
The means of attaching the filter externally to the outside of the camera does not need to be advanced. It could be as simple as wrapping elastic bands round the camera to hold the filter firmly onto the front.
Alternatively, you could try setting the Medium Density camera configuration preset to see if that improves your depth image results, as Medium Density provides a good balance between accuracy and the amount of detail on the depth image.
Python code for setting the Medium Density preset can be found at https://github.com/IntelRealSense/librealsense/issues/10695#issuecomment-1194400710 whilst the link below provides C++ code for setting a named preset.
OldLiu666
After completing the alignment of the depth stream and color stream, we can then obtain the depth data of each pixel, and then use the function rs.rs2_deproject_pixel_to_point(depth_intrin, [i, j], depth_value) to calculate the world coordinates of the pixel. However, for the depth map, there are always some pixels that have a depth value of 0 due to certain reasons, such as reflection, as shown in the black parts of the image below. The world coordinates calculated through the function rs.rs2_deproject_pixel_to_point(depth_intrin, [i, j], depth_value) for these pixels are (0,0,0). I want to know if there is any method to calculate the three-dimensional coordinates of all pixels. I have used hole_filling = rs.hole_filling_filter() on the depth map, but the results are not ideal. Some pixels have a depth value less than one, while some others suddenly become very large, greater than ten, which is not conducive for use.