🥻Our new #SIGGRAPH2025 paper introduces a novel pipeline that captures and digitizes physical threads, predicts fabric appearance from the weaving pattern, and aids designers in exploring new possibilities. 🔗 https://t.co/yxGj3mHuY3

How can one reconstruct the complete 3D interior of a wood block using only photos of its surfaces? 🪵 At SIGGRAPH'25 (Thursday!), Maria Larsson will present *Mokume*: a dataset of 190 diverse wood samples and a pipeline that solves this inverse texturing challenge. 🧵👇

Methods like NeRF and Gaussian Splats model the world as radioactive fog, rendered using alpha blending. This produces great results.. but are volumes the only way to get there?🤔 Our new SIGGRAPH'25 paper directly reconstructs surfaces without heuristics or regularizers.

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

While making some figures for SGI* this year, I made some "behind the scenes" footage of how they get made: https://t.co/V36jy2gZ2Z Basically a video extension of https://t.co/pxuocS9vP1 ("figures?") *SGI is a great program run by @JustinMSolomon & deserves more funding

Differentiable rendering made SIMPLE❗️ Differentiating physically based renderers is hard: Dirac-delta discontinuities arise at object silhouette. Our #SIGGRAPHAsia2024 work shows how a simple relaxation can rescue the day, enabling easy 3D reconstruction and relighting! (1/N)

While much has been said and written about this problem, current methods often perform poorly. We present *projective sampling*, a new method to accelerate derivative evaluation by orders of magnitude. This is joint work with Ziyi Zhang/@Ziyi_Zh and Nicolas Roussel/@njroussel.2/7