StereoCamera | Binocular ranging , that is , using the different parallax
kandi X-RAY | StereoCamera Summary
kandi X-RAY | StereoCamera Summary
Binocular ranging, that is, using the different parallax obtained by shooting the same object with the left and right cameras to judge the distance from the object to the camera, which involves algorithms such as camera calibration, epipolar recovery, correction, and stereo matching
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QUESTION
so I'm building an AR headset that uses stereo rendering to create 3d images. The only problem is that when the image reflects off the headset's reflectors it creates a flipped (mirror) image. I need to take account for that in my Three.js code so I was wondering if there are 2 options:
- Flip every 3d object in the scene
- Or flip the camera to simulate an optically inverted (mirror image) camera
I'll post the basics of my code here:
...ANSWER
Answered 2020-Apr-12 at 07:01Could you just use CSS to flip the element you're using to render the scene?
QUESTION
Can someone who understands stereo rendering give an explanation of what each of these functions is doing to create the VR stereo effect. There's little to no documentation on functions like StereoCamera(), setScissor(), setViewPort()
in the three.js library.
I would greatly appreciate any sort of high/low-level explanation.
Also, a bug I'm having is when I try to change the eyeSep value it has no effect on the final render.
...ANSWER
Answered 2020-Apr-06 at 08:14setScissor
and setViewport
set the area of the canvas to render to. More specifically setViewport
sets how to convert from a shader's clip space to some portion of the canvas's pixel space and setScissor
sets a rectangle outside of which nothing can be rendered.
See this.
Otherwise StereoCamera
just provides 2 cameras that are eyeSep
apart so you only have to manipulate one camera, the PerspectiveCamera
then you update
the StereoCamera
and it will automatically update the 2 eye cameras you can use for rendering.
QUESTION
I have a stereocamera setup where I use the OpenCV method cv::triangulatePoints to detect the checkeboard corners in 3D space. I was wondering what the method is to take these triangulated points and accurately estimate a 3D pose of the checkerboard.
One method I have encountered was found here, feeding the points into a PnP algorithm:
While this is a simple solution to my problem, I am not sure if this is completely correct, as most of my experience with method is for single camera use.
Any insight would be appreciated!
...ANSWER
Answered 2019-Jan-09 at 18:41Fit a plane to the points. The normal to the plane plus any two axes orthogonal to the normal and to each other define the pose of your checkerboard object with respect to the camera.
Note that for a checkerboard-like object you don't need stereo to find the orientation.
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