6 citations · 10 across the 3 of their papers we have counts for
7 papers
Matrix Decomposition on Graphs: A Functional View
Abhishek Sharma, Maks Ovsjanikov
We propose a functional view of matrix decomposition problems on graphs such as geometric matrix completion and graph regularized dimensionality reduction. Our unifying framework i…
Weakly Supervised Deep Functional Map for Shape Matching
Abhishek Sharma, Maks Ovsjanikov
A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization ter…
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov
We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate…
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Simone Melzi, Jing Ren, Emanuele Rodolà +3
We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our mai…
Unsupervised Deep Learning for Structured Shape Matching
Jean-Michel Roufosse, Abhishek Sharma, Maks Ovsjanikov
We present a novel method for computing correspondences across 3D shapes using unsupervised learning. Our method computes a non-linear transformation of given descriptor functions,…
Foreground Clustering for Joint Segmentation and Localization in Videos and Images
Abhishek Sharma
This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appe…