6 citations · 14 across the 17 of their papers we have counts for
19 papers
SurfR: Surface Reconstruction with Multi-scale Attention
Siddhant Ranade, Gonçalo Dias Pais, Ross Tyler Whitaker +3
We propose a fast and accurate surface reconstruction algorithm for unorganized point clouds using an implicit representation. Recent learning methods are either single-object repr…
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan +1
In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often…
Learning Deep Features for Shape Correspondence with Domain Invariance
Praful Agrawal, Ross T. Whitaker, Shireen Y. Elhabian
Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent cons…
Visualization of topology optimization designs with representative subset selection
Daniel J Perry, Vahid Keshavarzzadeh, Shireen Y Elhabian +3
An important new trend in additive manufacturing is the use of optimization to automatically design industrial objects, such as beams, rudders or wings. Topology optimization, as i…
GENs: Generative Encoding Networks
Surojit Saha, Shireen Elhabian, Ross T. Whitaker
Mapping data from and/or onto a known family of distributions has become an important topic in machine learning and data analysis. Deep generative models (e.g., generative adversar…
Unsupervised Shape Normality Metric for Severity Quantification
Wenzheng Tao, Riddhish Bhalodia, Erin Anstadt +3
This work describes an unsupervised method to objectively quantify the abnormality of general anatomical shapes. The severity of an anatomical deformity often serves as a determina…