  Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query
shape in a repository with models belonging to the same class, which requires
shape descriptors to be capable of representing detailed geometric information
to discriminate shapes with globally similar structures. Moreover, 3D objects
can be placed with arbitrary position and orientation in real-world
applications, which further requires shape descriptors to be robust to rigid
transformations. The shape descriptions used in existing 3D shape retrieval
systems fail to meet the above two criteria. In this paper, we introduce a
novel deep architecture, RISA-Net, which learns rotation invariant 3D shape
descriptors that are capable of encoding fine-grained geometric information and
structural information, and thus achieve accurate results on the task of
fine-grained 3D object retrieval. RISA-Net extracts a set of compact and
detailed geometric features part-wisely and discriminatively estimates the
contribution of each semantic part to shape representation. Furthermore, our
method is able to learn the importance of geometric and structural information
of all the parts when generating the final compact latent feature of a 3D shape
for fine-grained retrieval. We also build and publish a new 3D shape dataset
with sub-class labels for validating the performance of fine-grained 3D shape
retrieval methods. Qualitative and quantitative experiments show that our
RISA-Net outperforms state-of-the-art methods on the fine-grained object
retrieval task, demonstrating its capability in geometric detail extraction.
The code and dataset are available at: https://github.com/IGLICT/RisaNET.
