2 citations · 5 across the 4 of their papers we have counts for
4 papers
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan +5
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes. The key ingredient of our method is a neural skeleton-based representation encoding information on b…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Bo Sun, Vladimir G. Kim, Noam Aigerman +2
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative meth…
Batch Decorrelation for Active Metric Learning
Priyadarshini K, Ritesh Goru, Siddhartha Chaudhuri +1
We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object is more similar to object …