Search Results for author: Matheus Gadelha

Found 11 papers, 3 papers with code

Deep Manifold Prior

no code implementations8 Apr 2020 Matheus Gadelha, Rui Wang, Subhransu Maji

We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting from a random initialization.

Denoising Gaussian Processes

Learning Generative Models of Shape Handles

no code implementations CVPR 2020 Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji

We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.

Shape Reconstruction Using Differentiable Projections and Deep Priors

no code implementations ICCV 2019 Matheus Gadelha, Rui Wang, Subhransu Maji

We investigate the problem of reconstructing shapes from noisy and incomplete projections in the presence of viewpoint uncertainities.

3D Shape Reconstruction

Inferring 3D Shapes from Image Collections using Adversarial Networks

no code implementations11 Jun 2019 Matheus Gadelha, Aartika Rai, Subhransu Maji, Rui Wang

To this end, we present new differentiable projection operators that can be used by PrGAN to learn better 3D generative models.

A Deeper Look at 3D Shape Classifiers

no code implementations7 Sep 2018 Jong-Chyi Su, Matheus Gadelha, Rui Wang, Subhransu Maji

We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations.

3D Shape Classification Transfer Learning

Multiresolution Tree Networks for 3D Point Cloud Processing

no code implementations ECCV 2018 Matheus Gadelha, Rui Wang, Subhransu Maji

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks.

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

3 code implementations20 Jul 2017 Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang

The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.

3D Reconstruction 3D Shape Reconstruction

Shape Generation using Spatially Partitioned Point Clouds

no code implementations19 Jul 2017 Matheus Gadelha, Subhransu Maji, Rui Wang

We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework.

3D Shape Induction from 2D Views of Multiple Objects

no code implementations18 Dec 2016 Matheus Gadelha, Subhransu Maji, Rui Wang

In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints.

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