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- W4382364110 abstract "We propose Medial Atom Ray Fields (MARFs), a novel neural object representation that enables accurate differentiable surface rendering with a single network evaluation per camera ray. Existing neural ray fields struggle with multi-view consistency and representing surface discontinuities. MARFs address both using a medial shape representation, a dual representation of solid geometry that yields cheap geometrically grounded surface normals, in turn enabling computing analytical curvature despite the network having no second derivative. MARFs map a camera ray to multiple medial intersection candidates, subject to ray-sphere intersection testing. We illustrate how the learned medial shape quantities applies to sub-surface scattering, part segmentation, and aid representing a space of articulated shapes. Able to learn a space of shape priors, MARFs may prove useful for tasks like shape retrieval and shape completion, among others. Code and data can be found at https://github.com/pbsds/MARF." @default.
- W4382364110 created "2023-06-29" @default.
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- W4382364110 date "2023-06-01" @default.
- W4382364110 modified "2023-10-09" @default.
- W4382364110 title "MARF: The Medial Atom Ray Field object representation" @default.
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- W4382364110 doi "https://doi.org/10.1016/j.cag.2023.06.032" @default.
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