6-8, Av Blaise Pascal - Cité Descartes
Champs-sur-Marne
77455 Marne-la-Vallée cedex 2
France
✉ antoine (dot) guedon (at) enpc.fr
I am a PhD student in the IMAGINE computer vision team of École des Ponts ParisTech (ENPC) and I am co-advised by Vincent Lepetit (ENPC) and Pascal Monasse (ENPC). I am mostly interested in Image-Based Rendering (IBR) and 3D reconstruction using deep learning approaches. In particular, I worked on developing scalable approaches for simultaneously learning to reconstruct and explore 3D environments. More recently, I have been working on Novel View Synthesis and surface reconstruction in radiance fields. Before that, I studied at the Ecole polytechnique as well as the École normale supérieure Paris-Saclay where I obtained the MVA MS degree.
08-2024
Gaussian Frosting paper has been selected for an oral presentation at ECCV 2024!07-2024
Gaussian Frosting paper has been accepted to ECCV 2024!04-2024
I was invited by Pr. Ko Nishino from Kyoto University to work in Japan for 6 weeks. It really was a great experience!03-2024
I was invited by George Kopanas and Bernhard Kerbl to give a talk about Surface Reconstruction using Gaussian Splatting during the 3DV 2024 tutorial on Gaussian Splatting.02-2024
SuGaR paper has been accepted to CVPR 2024!03-2023
MACARONS paper has been accepted to CVPR 2023!11-2022
SCONE paper has received a Spotlight at NeurIPS 2022!08-2022
SCONE paper has been accepted to NeurIPS 2022!09-2021
I'm starting my PhD!ECCV 2024 (Oral)
We propose to represent surfaces by a mesh covered with a "Frosting" layer of varying thickness and made of 3D Gaussians. This representation captures both complex volumetric effects created by fuzzy materials such as hair or grass as well as flat surfaces. Built from RGB images only, it can be rendered in real-time and animated using traditional animation tools.@article{guedon2024frosting, title={Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering}, author={Gu{\'e}don, Antoine and Lepetit, Vincent}, journal={ECCV}, year={2024} }
CVPR 2024
We introduce a method that extracts accurate and editable meshes from 3D Gaussian Splatting representations within minutes on a single GPU. This enables easy editing, sculpting, rigging, animating, or relighting of the Gaussians using traditional softwares (Blender, Unity, Unreal Engine, etc.) by manipulating the mesh instead of the Gaussians themselves.@article{guedon2023sugar, title={SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering}, author={Gu{\'e}don, Antoine and Lepetit, Vincent}, journal={CVPR}, year={2024} }
CVPR 2023
We introduce a novel method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only, in a self-supervised fashion.@inproceedings{guedon2023macarons, title={{MACARONS: Mapping And Coverage Anticipation with RGB ONline Self-supervision}}, author={Gu{\'e}don, Antoine and Monnier, Tom and Monasse, Pascal and Lepetit, Vincent}, booktitle={{CVPR}}, year={2023}, }
NeurIPS 2022 (Spotlight)
We introduce a novel approach to solve the Next Best View problem for dense 3D reconstruction in unknown environments. Contrary to other learning-based methods, our approach scales to large 3D scenes and handles completely free camera motion at inference.@inproceedings{guedon2022scone, title={{SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration}}, author={Gu{\'e}don, Antoine and Monasse, Pascal and Lepetit, Vincent}, booktitle={{Advances in Neural Information Processing Systems}}, year={2022}, }
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