  Ongoing advancements in the fields of 3D modelling and digital archiving have
led to an outburst in the amount of data stored digitally. Consequently,
several retrieval systems have been developed depending on the type of data
stored in these databases. However, unlike text data or images, performing a
search for 3D models is non-trivial. Among 3D models, retrieving 3D
Engineering/CAD models or mechanical components is even more challenging due to
the presence of holes, volumetric features, presence of sharp edges etc., which
make CAD a domain unto itself. The research work presented in this paper aims
at developing a dataset suitable for building a retrieval system for 3D CAD
models based on deep learning. 3D CAD models from the available CAD databases
are collected, and a dataset of computer-generated sketch data, termed
'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the
components are also added to CADSketchNet. Using the sketch images from this
dataset, the paper also aims at evaluating the performance of various retrieval
system or a search engine for 3D CAD models that accepts a sketch image as the
input query. Many experimental models are constructed and tested on
CADSketchNet. These experiments, along with the model architecture, choice of
similarity metrics are reported along with the search results.
