Weakly-Supervised Deep Shape-From-Template
We propose WS-DeepSfT, a novel deep learning-based approach to the Shape-from-Template (SfT) problem, which aims at reconstructing the 3D shape of a deformable object from a single RGB image and a template. WS-DeepSfT addresses the limitations of existing SfT techniques by combining a weakly-supervi...
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Main Authors: | Sara Luengo-Sanchez, David Fuentes-Jimenez, Cristina Losada-Gutierrez, Daniel Pizarro, Adrien Bartoli |
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Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10854467/ |
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