  A laser scanner can easily acquire the geometric data of physical
environments in the form of a point cloud. Recognizing objects from a point
cloud is often required for industrial 3D reconstruction, which should include
not only geometry information but also semantic information. However,
recognition process is often a bottleneck in 3D reconstruction because it
requires expertise on domain knowledge and intensive labor. To address this
problem, various methods have been developed to recognize objects by retrieving
the corresponding model in the database from an input geometry query. In recent
years, the technique of converting geometric data into an image and applying
view-based 3D shape retrieval has demonstrated high accuracy. Depth image which
encodes depth value as intensity of pixel is frequently used for view-based 3D
shape retrieval. However, geometric data collected from objects is often
incomplete due to the occlusions and the limit of line of sight. Image
generated by occluded point clouds lowers the performance of view-based 3D
object retrieval due to loss of information. In this paper, we propose a method
of viewpoint and image resolution estimation method for view-based 3D shape
retrieval from point cloud query. Automatic selection of viewpoint and image
resolution by calculating the data acquisition rate and density from the
sampled viewpoints and image resolutions are proposed. The retrieval
performance from the images generated by the proposed method is experimented
and compared for various dataset. Additionally, view-based 3D shape retrieval
performance with deep convolutional neural network has been experimented with
the proposed method.
