Geometry Processing with Neural Fields
Dec 6, 2021
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Guandao Yang
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Serge Belongie
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Bharath Hariharan
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Vladlen Koltun
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Most existing geometry processing algorithms use meshes as the default shape1representation. Meshes, however, are hard to optimize for topology changes and usually require remeshing when undergoing large deformation. This paper instead proposes the use of neural implicit fields (NIFs) for geometry processing. NIFs can compactly store complicated shapes without spatial discretization. Moreover, NIFs are infinitely differentiable, which allows them to be optimized for objectives that involve higher-order derivatives. This paper develops loss functions and architectures to perform shape filtering and deformation with NIFs. We provide experimental results that showcase the applicability of NIFs to these tasksMost existing geometry processing algorithms use meshes as the default shape1representation. Meshes, however, are hard to optimize for topology changes and usually require remeshing when undergoing large deformation. This paper instead proposes the use of neural implicit fields (NIFs) for geometry processing. NIFs can compactly store complicated shapes without spatial discretization. Moreover, NIFs are infinitely differentiable, which allows them to be optimized for objectives that involve highe…
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NeurIPS 2021
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About NeurIPS 2021
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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