  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.
