  This paper uses clustering algorithms to introduce a shape framework for
deformable objects. Until now, the shape detection of the deformable objects
has faced several challenges: 1) unable to form a unified framework for
multiple shapes; 2) the calculation burden as a large number of calculations;
3) the inability to solve the 3D point-cloud case. A novel shape detection
framework for deformable objects is presented in this paper, which only uses
the input 2D-pixel data of the objects without any artificial markers. The
proposed detection approach runs in a highly real-time manner. For the
definitions of the shapes of the deformable objects, three shape configurations
are used to describe the outlines of the objects, i.e., centerline, contour,
and surface. In addition, for the obtaining of the 3D shape, Different from the
traditional 3D point cloud processing method, this article uses a one-to-one
mapping method between 2D-pixel points and 3D shape points. Therefore, this
guarantees a one-to-one correspondence between 2D and 3D shape points. Hence,
the proposed approach can enhance the autonomous capability to detect the shape
of deformable objects. Detailed experimental results are conducted within the
centerline configuration to evaluate the effectiveness of the proposed shape
detection framework.
