Abstract: In the field of fashion design, 3D Mannequin is a kind
of assisting tool which could rapidly realize the design concepts.
While the concept of 3D Mannequin is applied to the computer added
fashion design, it will connect with the development and the
application of design platform and system. Thus, the situation
mentioned above revealed a truth that it is very critical to develop a
module of 3D Mannequin which would correspond with the necessity
of fashion design. This research proposes a concrete plan that
developing and constructing a system of 3D Mannequin with Kinect.
In the content, ergonomic measurements of objective human features
could be attained real-time through the implement with depth camera
of Kinect, and then the mesh morphing can be implemented through
transformed the locations of the control-points on the model by
inputting those ergonomic data to get an exclusive 3D mannequin
model. In the proposed methodology, after the scanned points from the
Kinect are revised for accuracy and smoothening, a complete human
feature would be reconstructed by the ICP algorithm with the method
of image processing. Also, the objective human feature could be
recognized to analyze and get real measurements. Furthermore, the
data of ergonomic measurements could be applied to shape morphing
for the division of 3D Mannequin reconstructed by feature curves. Due
to a standardized and customer-oriented 3D Mannequin would be
generated by the implement of subdivision, the research could be
applied to the fashion design or the presentation and display of 3D
virtual clothes. In order to examine the practicality of research
structure, a system of 3D Mannequin would be constructed with JAVA
program in this study. Through the revision of experiments the
practicability-contained research result would come out.
Abstract: Subdivision surfaces were applied to the entire
meshes in order to produce smooth surfaces refinement from coarse
mesh. Several schemes had been introduced in this area to provide a
set of rules to converge smooth surfaces. However, to compute and
render all the vertices are really inconvenient in terms of memory
consumption and runtime during the subdivision process. It will lead
to a heavy computational load especially at a higher level of
subdivision. Adaptive subdivision is a method that subdivides only at
certain areas of the meshes while the rest were maintained less
polygons. Although adaptive subdivision occurs at the selected areas,
the quality of produced surfaces which is their smoothness can be
preserved similar as well as regular subdivision. Nevertheless,
adaptive subdivision process burdened from two causes; calculations
need to be done to define areas that are required to be subdivided and
to remove cracks created from the subdivision depth difference
between the selected and unselected areas. Unfortunately, the result
of adaptive subdivision when it reaches to the higher level of
subdivision, it still brings the problem with memory consumption.
This research brings to iterative process of adaptive subdivision to
improve the previous adaptive method that will reduce memory
consumption applied on triangular mesh. The result of this iterative
process was acceptable better in memory and appearance in order to
produce fewer polygons while it preserves smooth surfaces.
Abstract: Subdivision is a method to create a smooth surface from a coarse mesh by subdividing the entire mesh. The conventional ways to compute and render surfaces are inconvenient both in terms of memory and computational time as the number of meshes will increase exponentially. An adaptive subdivision is the way to reduce the computational time and memory by subdividing only certain selected areas. In this paper, a new adaptive subdivision method for triangle meshes is introduced. This method defines a new adaptive subdivision rules by considering the properties of each triangle's neighbors and is embedded in a traditional Loop's subdivision. It prevents some undesirable side effects that appear in the conventional adaptive ways. Models that were subdivided by our method are compared with other adaptive subdivision methods