Title:
3D shape descriptor for object recognition based on Kinect-like depth image
Author/Authors: Muhammad Amir As'ari, Usman Ullah Sheikh, Eko Supriyanto
Abstract:
3D shape descriptor has been used widely in the field of 3D object retrieval.
However, the performance of object retrieval greatly depends on the shape
descriptor used. The aims of this study is to review and compare the common 3D
shape descriptors proposed in 3D object retrieval literature for object recognition
and classification based on Kinect-like depth image obtained from RGB-D object
dataset. In this paper, we introduce (1) inter-class; and (2) intra-class evaluation in
order to study the feasibility of such descriptors in object recognition. Based on
these evaluations, local spin image outperforms the rest in discriminating different
classes when several depth images from an instance per class are used in interclass evaluation. This might be due to the slightly consistent local shape property
of such images and due to the proposed local similarity measurement that manages
to extract the local based descriptor. However, shape distribution performs
excellent for intra-class evaluation (that involves several instances per class) may
be due to the global shape from different instances per class is slightly unchanged.
These results indicate a remarkable feasibility analysis of the 3D shape descriptor
in object recognition that can be potentially used for Kinect-like sensor.