Depth-aware upsampling experiments (Part 5: Sample classification tweaks to improve the SSAO upsampling on surfaces)

This is another post of the series where I explain the ideas I try in order to improve the upsampling of the half-resolution SSAO render target of the VKDF sponza demo that was written by Iago Toral. In a previous post (3.2), I had classified the sample neighborhoods in surface neighborhoods and neighborhoods that contain depth discontinuities using the normals. Having this information about the neighborhoods, in the last post, I demonstrated how to further improve the nearest depth algorithm (that was explained in parts 1 and 2 of these series) and reduce the artifacts in the neighborhoods where we detect depth discontinuities. The result was good but we’ve seen that there are still some imperfections in a few edge cases. So, in this post, I am going to talk about some ideas I had to further improve the SSAO and my final decisions.

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