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.
6/n 🛡️How do we ensure no gaps?
Two complementary mechanisms:
1️⃣ Normal alignment loss: aligns rendered normals with image-space depth gradients.
2️⃣ Normal-aware densification: detects gaps in the shell, clones Gaussians with flipped normals, and closes the holes.
3/n ⛈️ The root problem: 3DGS treats each Gaussian as a symmetric blob of density.
But surfaces are asymmetric — they separate empty space from occupied matter. Modelling surface points with symmetric primitives introduces a fundamental reconstruction bias.
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!
Link to our talk.
sa2025.conference-schedule.org/presentation...