  In this paper, we proposed a method for reconstructing the 3D model based on
continuous sensory input. The robot can draw on extremely large data from the
real world using various sensors. However, the sensory inputs are usually too
noisy and high-dimensional data. It is very difficult and time consuming for
robot to process using such raw data when the robot tries to construct 3D
model. Hence, there needs to be a method that can extract useful information
from such sensory inputs. To address this problem our method utilizes the
concept of Object Semantic Hierarchy (OSH). Different from the previous work
that used this hierarchy framework, we extract the motion information using the
Deep Belief Network technique instead of applying classical computer vision
approaches. We have trained on two large sets of random dot images (10,000)
which are translated and rotated, respectively, and have successfully extracted
several bases that explain the translation and rotation motion. Based on this
translation and rotation bases, background subtraction have become possible
using Object Semantic Hierarchy.
