30 citations · 44 across the 4 of their papers we have counts for
9 papers
Deep Manifold Prior
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…
Learning Generative Models of Shape Handles
Matheus Gadelha, Giorgio Gori, Duygu Ceylan +5
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, s…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
Inferring 3D Shapes from Image Collections using Adversarial Networks
Matheus Gadelha, Aartika Rai, Subhransu Maji +1
We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our…
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradi…
A Deeper Look at 3D Shape Classifiers
Jong-Chyi Su, Matheus Gadelha, Rui Wang +1
We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial tra…