  High fidelity representation of shapes with arbitrary topology is an
important problem for a variety of vision and graphics applications. Owing to
their limited resolution, classical discrete shape representations using point
clouds, voxels and meshes produce low quality results when used in these
applications. Several implicit 3D shape representation approaches using deep
neural networks have been proposed leading to significant improvements in both
quality of representations as well as the impact on downstream applications.
However, these methods can only be used to represent topologically closed
shapes which greatly limits the class of shapes that they can represent. As a
consequence, they also often require clean, watertight meshes for training. In
this work, we propose DUDE - a Deep Unsigned Distance Embedding method which
alleviates both of these shortcomings. DUDE is a disentangled shape
representation that utilizes an unsigned distance field (uDF) to represent
proximity to a surface, and a normal vector field (nVF) to represent surface
orientation. We show that a combination of these two (uDF+nVF) can be used to
learn high fidelity representations for arbitrary open/closed shapes. As
opposed to prior work such as DeepSDF, our shape representations can be
directly learnt from noisy triangle soups, and do not need watertight meshes.
Additionally, we propose novel algorithms for extracting and rendering
iso-surfaces from the learnt representations. We validate DUDE on benchmark 3D
datasets and demonstrate that it produces significant improvements over the
state of the art.
