This is the second post of the OpenGL and Vulkan interoperability series, where I explain some EXT_external_objects and EXT_external_objects_fd use cases with examples taken by the Piglit tests I’ve written to test the extensions as part of my work for Igalia‘s graphics team.
We are going to see a very simple case of Vulkan/GL interoperability where an image is allocated using Vulkan and filled using OpenGL. This case is implemented in Piglit’s vk-image-overwrite test for images of different formats.
It’s been a while that Igalia’s graphics team had been working on the OpenGL extensions that provide the mechanisms for OpenGL and Vulkan interoperability in the Intel iris (gallium3d) driver that is part of mesa.
As there were no conformance tests (CTS) for this extension, and we needed to test it, we have written (and we are still writing) small tests for piglit that allow the exchange and the synchronization of the exchange of resources such as buffers, textures, and depth or stencil buffers.
It’s been some months I am only writing code for work (I am going to post soon about the cool things we are doing there) and have stopped the hobby projects. But this weekend (that was Easter for Greeks) there was a Ludum Dare jam with theme “Keep it alive”. Easter in quarantine was the perfect time for a break. 🙂
This year (2019) GUADEC took place in my city, Thessaloniki. Although I’ve never had any direct contributions to GNOME I wouldn’t miss the opportunity to attend the conference: the traveling time to the venue was about 15 minutes!
This post is mostly instructions on how to install the Temple OS on Debian using VirtualBox. Temple OS is an operating system written from scratch by Terry A. Davis in order to make programming and the creation of games more fun. Its shell is a Holy-C compiler where users can write commands in Holy-C and see the generated assembly. Temple OS lacks networking and security. Every program runs in ring-0 mode, which means that it has full access to the hardware. This is on purpose, “because it’s fun”. As Terry said, he didn’t want to write another UNIX and his goal was to create a modern Commodore64-like OS where the users can start writing code immediately after boot. If you are not familiar with the Temple OS and Terry A. Davis’s story, you can check the links at the end of this post.
This is the final post of the series where I explain the ideas tried 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 the previous posts, I performed experiments to explore different upsampling ideas and I explained the logic behind adopting or rejecting each one. At the end, I’ve managed to find a method that reduces the artifacts to an acceptable level. So, in this post I’ll try to present it completed and in detail.
Continue reading Depth-aware upsampling experiments (Part 6: A complete approach to upsample the half-resolution render target of a SSAO implementation)
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.
This is another post of the series where I explain some ideas I tried in order to improve the upscaling of the half-resolution SSAO render target of the VKDF sponza demo that was written by Iago Toral. In the previous post, 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 this post, I will try to further improve the nearest depth algorithm (see also parts 1 and 2) and reduce the artifacts in the neighborhoods where we detect depth discontinuities.