![]() This caused everything beyond the original length of the depth map to be randomly generated noise.Īfter solving this problem, a second problem arose, in that the 3D image was distorted by the anti-aliasing, causing more gradient issues than it was solving. The solution for the problem I was having, with the stereogram being contained to the left of the image, was caused by not extending the same array to reflect the larger depth map. How would I go about antialiasing the image hidden in the stereogram? My code is almost the same as the algorithm described in the paper, so an antialiasing algorithm based on that would be perfect. The rest is just random noise and cannot be viewed. For example, if I make the image 4 times bigger, the stereogram is limited to the left hand quarter of the image. However this seems to just contain the stereogram to the left of the image. I have tried using SSAA on the depth map and pattern and generating the stereogram, then reducing the image size again with an antialiasing filter. I'm looking to modify my current code to anti-alias the 3D image in order to smooth out the gradients even with a lower DPI. In the 175 DPI image, these are less pronounced but the guidance dots at the bottom of the image are further apart, and therefore viewing the 3D image is more difficult. On the 75 DPI image, distinct "triangles" of depth can be seen. While the detail of the depth can be increased by increasing the DPI, this becomes impractical as the convergence point becomes more difficult to reach. I believe this to be due to the DPI chosen when generating the image. The output is fairly good, though I have noticed that, even with a smooth gradient in the depth map, the output stereogram lacks these smooth gradients, instead having varying levels of depth. I recently completed some Python (2.7) code for generating random dot stereograms based on this paper.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |