Abstract
Learning discriminative shape representations is a
crucial issue for large-scale 3D shape retrieval.
In this paper, we propose the Collaborative Inner
Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class. Utilizing simple inner product operation, CIP loss explicitly enforces the features of the same class to
be clustered in a linear subspace, while inter-class
subspaces are constrained to be at least orthogonal.
Compared to previous metric loss functions, CIP
loss could provide more clear geometric interpretation for the embedding than Euclidean margin, and
is easy to implement without normalization operation referring to cosine margin. Moreover, our proposed loss term can combine with other commonly
used loss functions and can be easily plugged into
existing off-the-shelf architectures. Extensive experiments conducted on the two public 3D object
retrieval datasets, ModelNet and ShapeNetCore 55,
demonstrate the effectiveness of our proposal, and
our method has achieved state-of-the-art results on
both datasets.