Hsueh-Ti Derek Liu
@htderekliu
PhD student, Computer Science, University of Toronto
ID: 3327232544
https://www.dgp.toronto.edu/~hsuehtil/ 23-08-2015 21:17:05
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I’m recruiting multiple PhD students in my group at Penn Computer and Information Science in the following areas: - Neural Representations and rendering for 3D/4D Reconstruction - 3D Generative Models - Human Motion Generation - LLM guided Graphics and Vision - Neural Representations for Robotics etc.
📢Our new NVIDIA AI paper is out! I'm happy to share Compact NGP with Learned Hash Probing, which offers NeRFs that are 3.5x smaller than Instant NGP at a ~1.26x training cost and equivalent (or faster) inference speeds! 🌐: nv-tlabs.github.io/compact-ngp/ 🎥: youtube.com/watch?v=3TEry8…
Interested in learning about differential geometry and its connection to geometric computing? All material from the Carnegie Mellon University course on #DiscreteDifferentialGeometry has been collected in a new webpage (videos, code, exercises, etc.). Check it out! geometry.cs.cmu.edu/ddg
Generating nice meshes in AI pipelines is hard. Our #SIGGRAPHAsia2024 paper proposes a new representation which guarantees manifold connectivity, and even supports polygonal meshes -- a big step for downstream editing and simulation. (1/N) SpaceMesh: research.nvidia.com/labs/toronto-a…
My superstar student Mark Gillespie is on the academic job market 🤩: markjgillespie.com Beyond lots of beautiful, deep, award-winning research in geometry processing, he's just a terrific person who we already miss having around. Twist his arm if you want him to apply!
📢📢📢 "𝐑𝐚𝐝𝐢𝐚𝐧𝐭 𝐅𝐨𝐚𝐦: Real-Time Differentiable Ray Tracing", a mesh-based 3D represention. radfoam.github.io arxiv.org/abs/2502.01157 Co-lead by my PhD students Shrisudhan Govindarajan and Daniel Rebain, and w/ Kwang Moo Yi
After many years of giving talks, I no longer get nervous. Instead, I'm now nervous when my students give talks! Fortunately, they do an amazing job. Here's Mark Gillespie giving an extended talk on a new *harmonic* surface representation: youtube.com/watch?v=9h13FP…
Our #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” w/ Merlin Nimier-David Alec Jacobson Nick Sharp
Light version of Pierre Terdiman 's Zero-Byte-BVH here: skal65535.github.io/BVH/ The idea is to order the triangles as a binary tree by groups of 6 triangles, each group defines a bounding box to test. If hit, recurse both half of the remaining triangles. Corentin Wallez : WebGPU!
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! 🧵
Join us at the SIGGRAPH Canada Party hosted by Hsueh-Ti Derek Liu and me, on behalf of Canada Graphics Research! RSVP here: lu.ma/epm4b7xp #SIGGRAPH2025
📢ProcGen3D: Learning Neural Procedural Graphs for Image-to-3D Reconstruction Xinyi Zhang learns neural procedural graphs to generate high-fidelity 3D - MCTS-guided sampling maintains consistency with the input image, even from real images! Check it out: xzhang-t.github.io/project/ProcGe…