10 citations · 18 across the 30 of their papers we have counts for
6 papers · 1 filter
Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases
Jan Bednarik, Noam Aigerman, Vladimir G. Kim +4
We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically…
Differentiable Surface Triangulation
Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra +2
Triangle meshes remain the most popular data representation for surface geometry. This ubiquitous representation is essentially a hybrid one that decouples continuous vertex locati…
GLASS: Geometric Latent Augmentation for Shape Spaces
Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman +3
We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collec…
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri +4
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically me…
Neural Surface Maps
Luca Morreale, Noam Aigerman, Vladimir Kim +1
Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubi…
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung +3
We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a databas…