Ray marching is a common approach to GPU-accelerated volume rendering, but gives biased transmittance estimates. My new #SIGGRAPHAsia paper (+code) proposes an amazingly simple formula to eliminate this bias almost completely without using more samples.
momentsingraphics.de/SiggraphAsia...

🎄 Introducing our paper A Generalizable Light Transport 3D Embedding for Global Illumination lnkd.in/gQUMSAyV .
🙈 Just as Transformers learn long-range relationships between words or pixels, our new paper shows they can also learn how light interacts and bounces around a 3D scene.

Dr.Jit+Mitsuba just added support for fused neural networks, hash grids, and function freezing to eliminate tracing overheads. This significantly accelerates optimization &realtime workloads and enables custom Instant NGP and neural material/radiosity/path guiding projects. What will you do with it?

The latest development version of Dr.Jit now provides built-in support for evaluating and training MLPs (including fusing them into rendering workloads). They compile to efficient Tensor Core operations via NVIDIA's Cooperative Vector extension. Details: drjit.readthedocs.io/en/latest/nn...

Do you want to learn path tracing? Two lectures that I held at TU Delft are on YouTube now! The 2nd is what I had in mind for part 3 of my path tracing workshop. No Shadertoy exercises this time, but an open source Vulkan path tracer 🥳.
www.tudelft.nl/ewi/over-de-...
www.youtube.com/watch?v=Uerw...

More of the best moments in graphics on Bluesky ;) 🎉

Following over 1.5 years of hard work (w/@njroussel.bsky.social &@rtabbara.bsky.social), we just released a brand-new version of Dr.Jit (v1.0), my lab's differentiable rendering compiler along with an updated Mitsuba (v3.6). The list of changes is insanely long—here is what we're most excited about🧵