📢want to create realistic dynamic 3D worlds (>100 splats)?

my NVIDIA internship project, VoMP, is the first feed-forward approach turning surface geometry into volumetric sim-ready assets with real-world materials.

🌐Project: research.nvidia.com/labs/sil/pro...
📜Paper: arxiv.org/abs/2510.22975

The Spatial Intelligence Lab at NVIDIA (research.nvidia.com/labs/sil/) is looking for 2026 research interns! We do all kinds of cool work across graphics/vision, geometry, physics, & ML. Now is the time to apply & reach out!

nvidia.eightfold.ai/careers/job/... (not limited to Canada-only)

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

Also: this paper was recognized with a best paper award at SGP! Huge thanks to the organizers & congrats to the other awardees.

I was super lucky to work with Yousuf on this one, he's truly the mastermind behind it all!

Logarithmic maps are incredibly useful for algorithms on surfaces--they're local 2D coordinates centered at a given source.

Yousuf Soliman and I found a better way to compute log maps w/ fast short-time heat flow in "The Affine Heat Method" presented @ SGP2025 today! 🧵

Holding SIGGRAPH Asia 2026 in Malaysia is a slap in the face to the rights of LGBTQ+ people. Especially now, when underrepresented people need as much support as we can possibly give them ! Angry like me ? Sign this open letter to let them know. 🏳️‍⚧️🏳️‍🌈

docs.google.com/document/d/1...

Sampling points on an implicit surface is surprisingly tricky, but we know how to cast rays against implicit surfaces! There's a classic relationship between line-intersections and surface-sampling, which turns out to be quite useful for geometry processing.

My child’s doll and tools I captured as 3D Gaussians, turned digital with collisions and dynamics. We are getting closer to bridging the gap between the world we can touch and digital 3D. Experience the bleeding edge at #NVIDIA Kaolin hands-on lab, #CVPR2025! Wed, 8-noon. tinyurl.com/nv-kaolin-cv...

Check out Abhishek's research!

I was honestly surprised by this result: classic Barnes-Hut already builds a good spatial hierarchy for approximating kernel summations, but you can do even better by adding some stochastic sampling, for significant speedups on the GPU @ matching average error.

Selena's #Siggraph25 work found a simple, nearly one-line change that greatly eases neural field optimization for a wide variety of existing representations.

“Stochastic Preconditioning for Neural Field Optimization” by Selena Ling, Merlin Nimier-David, Alec Jacobson, & me.

Fun new paper at #SIGGRAPH2025:

What if instead of two 6-sided dice, you could roll a single "funky-shaped" die that gives the same statistics (e.g, 7 is twice as likely as 4 or 10).

Or make fair dice in any shape—e.g., dragons rather than cubes?

That's exactly what we do! 1/n

The Symposium on Geometry Processing is an amazing venue for geometry research: meshes, point clouds, neural fields, 3D ML, etc. Reviews are quick and high-quality.

The deadline is in ~10 days. Consider submitting your work, I'm planning to submit!

sgp2025.my.canva.site/submit-page-...

Hi BlueSky! I'm trying to get back to a "write" relationship with social media after hiding from it for a while.

I like geometry research, useful code, pierogies [sic], triangles, outdoorsy life, etc. I mainly post about research/software, but glad to chat about anything.