6 papers
From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians
Diego Gomez, Antoine Guédon, Nissim Maruani +2
3D Gaussian Splatting (3DGS) has revolutionized fast novel view synthesis, yet its opacity-based formulation makes surface extraction fundamentally difficult. Unlike implicit metho…
Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri +6
We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Ins…
MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction
Antoine Guédon, Diego Gomez, Nissim Maruani +3
While recent advances in Gaussian Splatting have enabled fast reconstruction of high-quality 3D scenes from images, extracting accurate surface meshes remains a challenge. Current…
ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion
Nissim Maruani, Wang Yifan, Matthew Fisher +2
This paper proposes ShapeShifter, a new 3D generative model that learns to synthesize shape variations based on a single reference model. While generative methods for 3D objects ha…
PoNQ: a Neural QEM-based Mesh Representation
Nissim Maruani, Maks Ovsjanikov, Pierre Alliez +1
Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applicatio…
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
Nissim Maruani, Roman Klokov, Maks Ovsjanikov +2
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge. In particular, while polygon meshes are arguably t…