We are seeing a Cambrian explosion of 3D shape representations for use in
machine learning. Some representations seek high expressive power in capturing
high-resolution detail. Other approaches seek to represent shapes as
compositions of simple parts, which are intuitive for people to understand and
easy to edit and manipulate. However, it is difficult to achieve both fidelity
and interpretability in the same representation. We propose DualSDF, a
representation expressing shapes at two levels of granularity, one capturing
fine details and the other representing an abstracted proxy shape using simple
and semantically consistent shape primitives. To achieve a tight coupling
between the two representations, we use a variational objective over a shared
latent space. Our two-level model gives rise to a new shape manipulation
technique in which a user can interactively manipulate the coarse proxy shape
and see the changes instantly mirrored in the high-resolution shape. Moreover,
our model actively augments and guides the manipulation towards producing
semantically meaningful shapes, making complex manipulations possible with
minimal user input.

提出 DualSDF 表示法，它使用两个层次的粒度对形状进行表达，并使用修正的变分目标通过共享的潜空间实现两个表示之间的紧密耦合。该模型不仅可以进行形状编辑和操作，而且能够指导用户进行语义感知的形状生成和变换。