Search Results for author: Marie-Julie Rakotosaona

Found 6 papers, 6 papers with code

Learning Delaunay Surface Elements for Mesh Reconstruction

1 code implementation CVPR 2021 Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov

We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.

Correspondence Learning via Linearly-invariant Embedding

1 code implementation NeurIPS 2020 Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov

However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better accuracy in challenging settings.

Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation

1 code implementation ECCV 2020 Marie-Julie Rakotosaona, Maks Ovsjanikov

We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties.

OperatorNet: Recovering 3D Shapes From Difference Operators

1 code implementation ICCV 2019 Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov

This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

1 code implementation4 Jan 2019 Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers.


Cannot find the paper you are looking for? You can Submit a new open access paper.