Fine-grained 3D object recognition: an approach and experiments
Junhyung Jo Hamidreza Kasaei
Three-dimensional (3D) object recognition technology is being used as a core
technology in advanced technologies such as autonomous driving of automobiles.
There are two sets of approaches for 3D object recognition: (i) hand-crafted
approaches like Global Orthographic Object Descriptor (GOOD), and (ii) deep
learning-based approaches such as MobileNet and VGG. However, it is needed to
know which of these approaches works better in an open-ended domain where the
number of known categories increases over time, and the system should learn
about new object categories using few training examples. In this paper, we
first implemented an offline 3D object recognition system that takes an object
view as input and generates category labels as output. In the offline stage,
instance-based learning (IBL) is used to form a new category and we use K-fold
cross-validation to evaluate the obtained object recognition performance. We
then test the proposed approach in an online fashion by integrating the code
into a simulated teacher test. As a result, we concluded that the approach
using deep learning features is more suitable for open-ended fashion. Moreover,
we observed that concatenating the hand-crafted and deep learning features
increases the classification accuracy.