Learning Shape Templates With Structured Implicit Functions
@article{Genova2019LearningST, title={Learning Shape Templates With Structured Implicit Functions}, author={Kyle Genova and F. Cole and D. Vlasic and Aaron Sarna and W. Freeman and T. Funkhouser}, journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2019}, pages={7153-7163} }
Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of… CONTINUE READING
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References
SHOWING 1-10 OF 71 REFERENCES
Parsing Geometry Using Structure-Aware Shape Templates
- Computer Science
- 2018 International Conference on 3D Vision (3DV)
- 2018
- 13
- PDF
3D ShapeNets: A deep representation for volumetric shapes
- Computer Science
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2015
- 2,121
- PDF
Learning part-based templates from large collections of 3D shapes
- Computer Science
- ACM Trans. Graph.
- 2013
- 185
- PDF
Learning Implicit Fields for Generative Shape Modeling
- Computer Science
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
- 236
- PDF
Learning Shape Abstractions by Assembling Volumetric Primitives
- Computer Science
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
- 146
- Highly Influential
- PDF
Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Computer Science
- 2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
- 1,468
- PDF
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
- Computer Science
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
- 374
- PDF
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
- Computer Science, Mathematics
- 2017 IEEE International Conference on Computer Vision (ICCV)
- 2017
- 86
- PDF