Diego Gomez
@diegoxgomez.bsky.social
PhD student @ LIX | BX 21 | MVA 23
https://diego1401.github.io
https://diego1401.github.io
Pinned
Diego Gomez
@diegoxgomez.bsky.social
· Apr 9
1/n 🧵 Introducing Gaussian Wrapping — a principled framework for extracting high-quality meshes from 3DGS! 🚲
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
1/n 🧵 Introducing Gaussian Wrapping — a principled framework for extracting high-quality meshes from 3DGS! 🚲
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
April 9, 2026 at 1:13 PM
1/n 🧵 Introducing Gaussian Wrapping — a principled framework for extracting high-quality meshes from 3DGS! 🚲
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
I’ll be at #SIGGRAPHAsia2025 next week presenting our paper MILo! Join the Neural Fields and Surface Reconstruction session on Tuesday, December 16.
If you’ll be in Hong Kong and would like to discuss research, or grab a coffee ☕️ feel free to reach out.
If you’ll be in Hong Kong and would like to discuss research, or grab a coffee ☕️ feel free to reach out.
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
December 14, 2025 at 6:37 AM
I’ll be at #SIGGRAPHAsia2025 next week presenting our paper MILo! Join the Neural Fields and Surface Reconstruction session on Tuesday, December 16.
If you’ll be in Hong Kong and would like to discuss research, or grab a coffee ☕️ feel free to reach out.
If you’ll be in Hong Kong and would like to discuss research, or grab a coffee ☕️ feel free to reach out.
Reposted by Diego Gomez
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
September 8, 2025 at 11:35 AM
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇