In this paper, we analyse the process of designing a Content-
Based 3D shape Retrieval (CB3DR) adapted for non-experts.
Our CB3DR solution aims at scanning an object on the fly
with a low-cost 3D sensor and retrieve similar shapes from
a database using the 3D point cloud acquired. Our system
should meet the requirements of archaeologists who would
like to be able to acquire artefacts without prior expertise in
scanning, then query easily from the field knowledge bases
for Cultural Heritage, and thus retrieve artefacts (i.e. objects
or parts of objects) with similar shape without carrying or
even moving the artefact found on the site. This context is
definitely a retrieval context rather than a classification one
since the found artefact may be unknown.
At the era of Deep Learning techniques, we investigate in
this paper how to design a system which could benefit from
the latest advances in 3D object recognition. We thus design
a classic CBR workflow starting with a similarity search step
followed by an interactive learning SVM which requires very
simple positive-negative annotations from the users to refine
the ranking provided by the similarity search. The 3D shapes
are first described using data representations. We compare
first the “old fashion” hand-crafted 3D descriptors (which do
not require to be learnt or trained) with PointNet, one of the
most recent 3D object deep representation. We evaluate our
retrieval baseline against recent Deep Learning approaches on
ModelNet10 and explore the potential of deep representations
in our interactive learning framework.