  Sketch-based 3D shape retrieval is a challenging task due to the large domain
discrepancy between sketches and 3D shapes. Since existing methods are trained
and evaluated on the same categories, they cannot effectively recognize the
categories that have not been used during training. In this paper, we propose a
novel domain disentangled generative adversarial network (DD-GAN) for zero-shot
sketch-based 3D retrieval, which can retrieve the unseen categories that are
not accessed during training. Specifically, we first generate domain-invariant
features and domain-specific features by disentangling the learned features of
sketches and 3D shapes, where the domain-invariant features are used to align
with the corresponding word embeddings. Then, we develop a generative
adversarial network that combines the domain-specific features of the seen
categories with the aligned domain-invariant features to synthesize samples,
where the synthesized samples of the unseen categories are generated by using
the corresponding word embeddings. Finally, we use the synthesized samples of
the unseen categories combined with the real samples of the seen categories to
train the network for retrieval, so that the unseen categories can be
recognized. In order to reduce the domain shift problem, we utilized unlabeled
unseen samples to enhance the discrimination ability of the discriminator. With
the discriminator distinguishing the generated samples from the unlabeled
unseen samples, the generator can generate more realistic unseen samples.
Extensive experiments on the SHREC'13 and SHREC'14 datasets show that our
method significantly improves the retrieval performance of the unseen
categories.
