11/11 📚 Resources:
📄 Paper: arxiv.org/abs/2506.24096
💻 Code: github.com/Anttwo/MILo
🌐 Project Page: anttwo.github.io/milo/

Huge thanks to my amazing co-authors and the supporting institutions! 🙏

10/n📺Video:
See MILo in action!
Our presentation video showcases the differentiable pipeline and reconstruction results across various scenes.

🔗 YouTube video: www.youtube.com/watch?v=rOBs...

9/n🎮Interactive Gallery:
Check out our interactive, online 3D viewer with both mesh and Gaussian representations!

🔗 Gallery: anttwo.github.io/milo/#galler...

8/n📈Optional depth-order regularization:
For even cleaner backgrounds, we propose an optional loss using DepthAnythingV2 that enforces depth ordering consistency.

This drastically improves background geometry quality!

7/n🎨Animation & Editing:
Since Gaussians align with the extracted mesh surface, any mesh modification can easily be propagated to the Gaussians!

We include in the code a Blender addon for easy editing and animation - no coding required.

6/n🔧Plug-and-play design:
MILo can be integrated into any Gaussian Splatting pipeline!

We provide simple differentiable functions that take Gaussian parameters as input and return meshes.

Perfect for adding differentiable surface processing to your 3DGS projects!

5/n🎯Scalability advantage:
MILo reconstructs full scenes including all background elements, not just foregrounds.

To achieve this efficiency, we select only surface-likely Gaussians by repurposing the importance sampling from Mini-Splatting2.

4/n📊Results:
✅ Higher quality meshes with significantly fewer vertices
✅ 60-350MB mesh sizes (vs GBs in other methods)
✅ Complete scene reconstruction (including backgrounds)
✅ Better performance on benchmarks

Efficiency meets quality!

3/n🏗️How MILo works:
1️⃣ Each Gaussian spawns pivots
2️⃣ Delaunay triangulation connects pivots
3️⃣ SDF values assigned to pivots
4️⃣ Differentiable Marching Tetrahedra extracts mesh

The pipeline is differentiable, enabling mesh supervision to improve Gaussian configurations!

2/n🔗Key innovation: differentiable mesh extraction at every training iteration

Unlike previous methods, MILo extracts vertex locations and connectivity purely from Gaussian parameters, allowing gradient flow from mesh back to Gaussians. This creates a powerful feedback loop!

#CVPR2025 Fri June 13 (PM) ✨ Highlight
🍵 MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views
@antoine-guedon.bsky.social @kyotovision.bsky.social
📄 pdf: arxiv.org/abs/2412.06767
🌐 webpage: anttwo.github.io/matcha/

I actually saw him dancing on a bench 😱
anttwo.github.io/frosting/

And the fact that this pipeline makes it possible to get sharp meshes from sparse unposed imgs means 2 things:

1. MASt3R-SfM is so good, it's crazy... I love it.

2. The regularization we introduce seems to really help the representation to stabilize, even though the constraints are very sparse

You're entirely right!
And actually MASt3R-SfM does the tougher part of the job, clearly 😁
I just meant that both can be used in a unified pipeline for getting sharp meshes from unposed images.

This work was done in collaboration with Tomoki Ichikawa, Kohei Yamashita and Professor Ko Nishino from @kyotovision.bsky.social well as @imagineenpc.bsky.social

🌐Website: anttwo.github.io/matcha/

💻Code: github.com/Anttwo/MAtCha

🔑 Key point #3: We also introduce a novel “depth-order” regularization that leverages depth maps estimated with a monodepth estimator.

The depth maps can be multi-view inconsistent, no problem!

MAtCha still gets smooth, detailed background while preserving foreground details.

🔑 Key point #2: Inspired by Gaussian Opacity Fields, we developed a new mesh extraction method for 2DGS.

It properly handles both foreground and background geometry while being lightweight if needed (only 150-350MB).

No post-processing mesh decimation is required!

🔑 Key point #1: Our novel optimization pipeline is robust to sparse-view inputs (as few as 3 to 10 images) but also scales to dense-view scenarios (hundreds of views).

No more choosing between sparse or dense methods!

MAtCha introduces a novel surface representation that reconstructs high-quality 3D meshes with photorealistic rendering from just a handful of images.

💡Our key idea: model scene geometry as an Atlas of Charts and refine it with 2D Gaussian surfels.