  This paper is motivated from a fundamental curiosity on what defines a
category of object shapes. For example, we may have the common knowledge that a
plane has wings, and a chair has legs. Given the large shape variations among
different instances of a same category, we are formally interested in
developing a quantity defined for individual points on a continuous object
surface; the quantity specifies how individual surface points contribute to the
formation of the shape as the category. We term such a quantity as
category-level shape saliency or shape saliency for short. Technically, we
propose to learn saliency maps for shape instances of a same category from a
deep implicit surface network; sensible saliency scores for sampled points in
the implicit surface field are predicted by constraining the capacity of input
latent code. We also enhance the saliency prediction with an additional loss of
contrastive training. We expect such learned surface maps of shape saliency to
have the properties of smoothness, symmetry, and semantic representativeness.
We verify these properties by comparing our method with alternative ways of
saliency computation. Notably, we show that by leveraging the learned shape
saliency, we are able to reconstruct either category-salient or
instance-specific parts of object surfaces; semantic representativeness of the
learned saliency is also reflected in its efficacy to guide the selection of
surface points for better point cloud classification.
