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!

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.

DUSt3R et al. are impressive, but how do they actually work? We investigate this in our project 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘔𝘶𝘭𝘵𝘪-𝘝𝘪𝘦𝘸 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴!⁣ We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️

Stary and Gaubil et al., "Understanding multi-view transformers" We use Dust3r as a black box. This work looks under the hood at what is going on. The internal representations seem to "iteratively" refine towards the final answer. Quite similar to what goes on in point cloud net

Hey there! I'll be presenting Diffumatch this afternoon. Come at poster Booth 67 if you want to learn more ! bsky.app/profile/daid...

📢 Excited to share our latest #ICCV2025 work DiffuMatch: learning spectral diffusion priors for robust non-rigid shape matching! (1/n)

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. 🧵👇