11–19 of 19 posts
If you enjoyed this paper, there's a companion blog post about the actual process of writing it: https://www.mattkeeter.com/projects/siggraph/
(and I'm happy to answer questions, of course)
Very very impressive work. I wonder if it would be feasible for vendors to implement the interpreter in hardware in future.
In another project [1], I found a 2-6x speedup in going from an interpreter to a fully-compiled shader, so this can make a huge difference!
Fabulous work. The video presentation is only 18 minutes long, well organized, and very accessible -- the author does a great job of explaining how and why the rendering works so efficiently using a simple example in 2D: https://www.youtube.com/watch?v=_6CnaugAcCc Highly recommended.
The 2D part is well explained, however, I am interested in how they made it 3D. They have voxels, but the actual rendering is a bit unclear. I guess they simply didn't do much work on that part, using some bruteforce-ish raymarching technique, which their fast evaluation and nicely bound objects allows. They mention further work though, like sparse voxel octrees, improving culling, etc... So I guess that will be for…
Same priciple should work for anything with decently good distance function, reifying a mesh or SVO could always be combined with rough culling of the "tapes" since it's all about the distances.
how do people that absolutely need the lowest latency numbers make do with GPUs? i'm not in graphics but i'm in ML and lately i've been working on research to squeeze as much juice out GPUs as possible. my last project involved optimizing a pipeline that basically consisted of just a stack of filters and some min/max finding. the fastest i could get it to go after throwing everything i could at it and i only got it down to ~20ms. that's ~50Hz. admittedly it was a tall stack but still i don't understand how game devs (for example) get complex pipelines to finish within the 60Hz/16ms given you don't never have access to bare metal GPU.
this isn't exactly the right place to ask but i'm betting people interested in graphics pipeline latency will visit this comment section: how do people that absolutely need the lowest latency numbers make do with GPUs? i'm not in graphics but i'm in ML and lately i've been working on research to squeeze as much juice out GPUs as possible. my last project involved optimizing a pipeline that basically consisted of just…
Author here, I'm glad to have finally figured out why a bunch of people followed me on Twitter this morning! If you enjoyed this paper, there's a companion blog post about the actual process of writing it: https://www.mattkeeter.com/projects/siggraph/ (and I'm happy to answer questions, of course)
Author here, I'm glad to have finally figured out why a bunch of people followed me on Twitter this morning! If you enjoyed this paper, there's a companion blog post about the actual process of writing it: https://www.mattkeeter.com/projects/siggraph/ (and I'm happy to answer questions, of course)
This certainly looks like a very interesting approach! As I'm not in the field of graphics would you be able to comment how this compares to cone marching and how well it can render fractal like surfaces?
The downside to sphere tracing and similar is that it limits the input model: you have to guarantee that evaluating the model at [x, y, z] gives you a result that's less-than-or-equal to the true (Euclidean) distance to the shape's surface.
(or a distance adjusted by some constant scaling factor, i.e. Lipshitz continuity [2])
This is a relatively fragile property, and really limits what kinds of shapes and transformations you can use when modeling.
Using interval arithmetic is more robust against arbitrary models, at the cost of being less efficient when models are well-behaved.
I don't know much about state-of-the-art fractal rendering! I'd imagine that the fixed (original) tape in MPR would be a limitation here, because you may want to terminate conditionally, rather than evaluating a fixed expression.
[1] http://www.fulcrum-demo.org/wp-content/uploads/2012/04/Cone_...
This is important for vision ML because sufficiently advanced simulations may comprise the real world as a special case: https://arxiv.org/abs/1703.06907 For example, gaming pixel maps have been used for semantic simulation, and have been rendered scenes: https://arxiv.org/abs/1608.02192 https://www.cv-foundation.org/openaccess/content_cvpr_2016/p... This concept has not (yet) been applied in audio ML. We have a pape…
How is it related to ML? Closed form implicit surfaces are actually one of the most difficult way to model the real world. They are neat because they are very compact and the creation process is close to modeling with (mathematical) clay. But they are hard to use if you want to model the real world with all its complexity, resulting of a variety of chemical and physical processes happening over time. There is a reaso…
Very very impressive work. I wonder if it would be feasible for vendors to implement the interpreter in hardware in future.
Being able to do very basic JITting on the GPU would be a great first step – load/store operations when evaluating the tapes are terrible for memory access, since they use global memory rather than registers. In another project [1], I found a 2-6x speedup in going from an interpreter to a fully-compiled shader, so this can make a huge difference! [1] https://www.mattkeeter.com/projects/rayray/