Every lens leaves a blur signature—a hidden fingerprint in every photo. In our new #TPAMI paper, we show how to learn it fast (5 mins of capture!) with Lens Blur Fields ✨ With it, we can tell apart ‘identical’ phones by their optics, deblur images, and render realistic blurs.

Code is now out! Try it for yourself here: github.com/abhimadan/st...

GitHub - abhimadan/stochastic-barnes-hut: A reference implementation of the SIGGRAPH 2025 paper, "Stochastic Barnes-Hut Approximation for Fast Summation on the GPU".

A reference implementation of the SIGGRAPH 2025 paper, "Stochastic Barnes-Hut Approximation for Fast Summation on the GPU". - abhimadan/stochastic-barnes-hut

github.com

Abhishek Madan@abhishekmadan.bsky.social · last yr.

At SIGGRAPH 2025, we’ll be presenting the paper “Stochastic Barnes-Hut Approximation for Fast Summation on the GPU”. By injecting a bit of randomization into the classic yet deterministic Barnes-Hut approximation for fast kernel summation, we can achieve nearly 10x speedups on the GPU!

Our #SGP25 work studies a simple and effective way to uniformly sample implicit surfaces by casting rays. (1/9) “Uniform Sampling of Surfaces by Casting Rays” w/ @abhishekmadan.bsky.social @nmwsharp.bsky.social and Alec Jacobson

At SIGGRAPH 2025, we’ll be presenting the paper “Stochastic Barnes-Hut Approximation for Fast Summation on the GPU”. By injecting a bit of randomization into the classic yet deterministic Barnes-Hut approximation for fast kernel summation, we can achieve nearly 10x speedups on the GPU!