With the work on the new input system and the new deployment system coming to a close, it is high time we shift gears and focus our efforts on bundling all this into a shiny new release. So with an eye toward a final release of Panda3D 1.10, most of the work in May has centered around improving the engine’s stability and cleaning up the codebase.
As such, many bugs and regressions have been fixed that are too numerous to name. I’m particularly proud to declare the multithreaded render pipeline significantly more stable than it was in 1.9. We have also begun to make better use of compiler warnings and code-checking tools. This has led us find bugs in the code that we did not even know existed!
We announced two months ago that we were switching the minimum version of the Microsoft Visual C++ compiler from 2010 to 2015. No objections to this have come in, so this move has been fully implemented in the past month. This has cleared the way for us to make use of C++11 to the fullest extent, allowing us to write more robust code and spend less of our time writing compiler-specific code or maintaining our own threading library, which ultimately results in a better engine for you.
Behind the scenes, many design discussions have been taking place regarding our plans for the Panda3D release that will follow 1.10. In particular, I’d like to highlight a proposed new abstraction for describing multi-pass rendering that has begun to take shape.
Multi-pass rendering is a technique to render a scene in multiple ways before compositing it back together into a single rendered image. The current way to do this in Panda3D hinges on the idea of a “graphics buffer” being similar to a regular on-screen window, except of course that it does not appear on screen. At the time this feature was added, this matched the abstractions of the underlying graphics APIs quite well. However, it is overly cumbersome to set up for some of the most common use cases, such as adding a simple post-processing effect to the final rendered image. More recent additions like FilterManager and the RenderPipeline’s RenderTarget system have made this much easier, but these are high-level abstractions that simply wrap around the same underlying low-level C++ API, which still does not have an ideal level of control over the rendering pipeline.
That last point is particularly relevant in our efforts to provide the most optimal level of support for Oculus Rift and for the Vulkan rendering API. For reasons that go beyond the scope of this post, implementing these in the most optimal fashion will require Panda3D to have more complete knowledge of how all the graphics buffers in the application fit together to produce the final render result, which the current API makes difficult.
To remedy this, the proposed approach is to let the application simply describe all the rendering passes up-front in a high-level manner. You would graph out how the scene is rendered by connecting the inputs and outputs of all the filters and shaders that should affect it, similar to Blender’s compositing nodes. You would no longer need to worry about setting up all the low-level buffers, attachments, cameras and display regions. This would all be handled under the hood, enabling Panda3D to optimize the setup to make better use of hardware resources. We could even create a file format to allow storing such a “render blueprint” in a file, so something like loading and enabling a deferred rendering pipeline would be possible in only a few lines of code!
This is still in the early design stages, so we will talk about these things in more detail as we continue to iron out the design. If you have ideas of your own to contribute, please feel free to share them with us!
In the meantime, we will continue to work towards a final release of 1.10. And this is the time when you can shine! If you wish to help, you are encouraged to check out a development build of Panda3D from the download page (or installed via our custom pip index) and try it with your projects. If you encounter an issue, please go to the issue tracker on GitHub and let us know!