Antoine Guédon
@antoine-guedon.bsky.social
PhD student in computer vision at Imagine, ENPC - @imagineenpc.bsky.social
I'm interested in 3D Reconstruction, Radiance Fields, Gaussian splatting, 3D Scene Rendering, 3D Scene Understanding, etc.
Webpage: https://anttwo.github.io/
I'm interested in 3D Reconstruction, Radiance Fields, Gaussian splatting, 3D Scene Rendering, 3D Scene Understanding, etc.
Webpage: https://anttwo.github.io/
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.
🧵👇
I actually saw him dancing on a bench 😱
anttwo.github.io/frosting/
anttwo.github.io/frosting/
April 3, 2025 at 3:58 PM
I actually saw him dancing on a bench 😱
anttwo.github.io/frosting/
anttwo.github.io/frosting/
🔑 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.
The depth maps can be multi-view inconsistent, no problem!
MAtCha still gets smooth, detailed background while preserving foreground details.
April 3, 2025 at 10:33 AM
🔑 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.
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!
It properly handles both foreground and background geometry while being lightweight if needed (only 150-350MB).
No post-processing mesh decimation is required!
April 3, 2025 at 10:33 AM
🔑 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!
It properly handles both foreground and background geometry while being lightweight if needed (only 150-350MB).
No post-processing mesh decimation is required!
After aligning our charts, we refine them using Gaussian Splatting rendering.
Gaussians are constrained to stay close to our charts, preventing them from diverging in this sparse-view scenario.
(👇3 training images)
Gaussians are constrained to stay close to our charts, preventing them from diverging in this sparse-view scenario.
(👇3 training images)
December 11, 2024 at 2:59 PM
After aligning our charts, we refine them using Gaussian Splatting rendering.
Gaussians are constrained to stay close to our charts, preventing them from diverging in this sparse-view scenario.
(👇3 training images)
Gaussians are constrained to stay close to our charts, preventing them from diverging in this sparse-view scenario.
(👇3 training images)
🗺️We initialize the charts with DepthAnythingV2 and deform them with a novel neural deformation model.
⚠️Depth maps contain many refined details but have inaccurate scale; Our deformation model aims to solve this problem!
(👇5 training images)
⚠️Depth maps contain many refined details but have inaccurate scale; Our deformation model aims to solve this problem!
(👇5 training images)
December 11, 2024 at 2:59 PM
🗺️We initialize the charts with DepthAnythingV2 and deform them with a novel neural deformation model.
⚠️Depth maps contain many refined details but have inaccurate scale; Our deformation model aims to solve this problem!
(👇5 training images)
⚠️Depth maps contain many refined details but have inaccurate scale; Our deformation model aims to solve this problem!
(👇5 training images)
💡Our key idea is to model the scene geometry as an Atlas of Charts, rendered with Gaussians.
🗺️Each input image is converted into a optimizable chart.
👇In this video, you can see the charts flying and aligning together (10 training images)!
🗺️Each input image is converted into a optimizable chart.
👇In this video, you can see the charts flying and aligning together (10 training images)!
December 11, 2024 at 2:59 PM
💡Our key idea is to model the scene geometry as an Atlas of Charts, rendered with Gaussians.
🗺️Each input image is converted into a optimizable chart.
👇In this video, you can see the charts flying and aligning together (10 training images)!
🗺️Each input image is converted into a optimizable chart.
👇In this video, you can see the charts flying and aligning together (10 training images)!
⚠️Reconstructing sharp 3D meshes from a few unposed images is a hard and ambiguous problem.
☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵
🌐Webpage: anttwo.github.io/matcha/
☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵
🌐Webpage: anttwo.github.io/matcha/
December 11, 2024 at 2:59 PM
⚠️Reconstructing sharp 3D meshes from a few unposed images is a hard and ambiguous problem.
☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵
🌐Webpage: anttwo.github.io/matcha/
☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵
🌐Webpage: anttwo.github.io/matcha/