Depth-aware upsampling experiments (Part 3.2: Improving the upsampling using normals to classify the samples)

This post is again about improving the upsampling of the half-resolution SSAO render target used in the VKDF sponza demo that was written by Iago Toral. I am going to explain how I used information from the normals to understand if the samples of each 2×2 neighborhood we check during the upsampling belong to the same surface or not, and how this was useful in the upsampling improvement.

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Depth-aware upsampling experiments (Part 3.1: Improving the upsampling using depths to classify the samples)

In my previous posts of these series I analyzed the basic idea behind the depth-aware upsampling techniques. In the first post [1], I implemented the nearest depth sampling algorithm [3] from NVIDIA and in the second one [2], I compared some methods that are improving the quality of the z-buffer downsampled data that I use with the nearest depth. The conclusion was that the nearest depth sampling alone is not good enough to reduce the artifacts of Iago Toral’s SSAO implementation in VKDF [4] to an acceptable level. So, in this post, I am going to talk about my early experiments to further improve the upsampling and the logic behind each one. I named it part 3.1 because while having started the series I’ve found that some combinations of these methods with other ones can give quite better visual results, and as my experiments with the upsampling techniques cannot fit one blog post, I am going to split the upscaling improvements (part 3) in sub-parts.

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