  This paper presents a deep learning approach for the classification of
Engineering (CAD) models using Convolutional Neural Networks (CNNs). Owing to
the availability of large annotated datasets and also enough computational
power in the form of GPUs, many deep learning-based solutions for object
classification have been proposed of late, especially in the domain of images
and graphical models. Nevertheless, very few solutions have been proposed for
the task of functional classification of CAD models. Hence, for this research,
CAD models have been collected from Engineering Shape Benchmark (ESB), National
Design Repository (NDR) and augmented with newer models created using a
modelling software to form a dataset - 'CADNET'. It is proposed to use a
residual network architecture for CADNET, inspired by the popular ResNet. A
weighted Light Field Descriptor (LFD) scheme is chosen as the method of feature
extraction, and the generated images are fed as inputs to the CNN. The problem
of class imbalance in the dataset is addressed using a class weights approach.
Experiments have been conducted with other signatures such as geodesic distance
etc. using deep networks as well as other network architectures on the CADNET.
The LFD-based CNN approach using the proposed network architecture, along with
gradient boosting yielded the best classification accuracy on CADNET.
