mobile off
ABOUT US  |  RESEARCH  |  PUBLICATION   |  COURSE  |  SEMINAR  |  LINKS
Research
Physically-Based Simulation
Fluid Interaction
Fire
Ice and Snow
Water
Smoke
Shell Deformation
User Behavior Analysis
Sentiment Analysis
Outlier Detection
Automatic Player Behavior Analysis
Adaptive Agent Navigation
Crowd Simulation
Graphics Applications
Neuroimage
Artificial Life
3D Geometry Processing
Real domain data visualization
Integral MLS Surface Model
SDF-based Geometry Synthesis
Point-based Geometry and Modeling
Texture Processing
Point-based Geometry and Modeling

1. Research Overview

 Recent advances in scanning technology have led to a rapid increase in the availability and size of geometric data sets.  Some consist of hundreds of millions of points.  Point data is usually considered by generating and simplifying a triangular mesh.  Meshes obtained from scan data are usually too large to be displayed in real-time.  The researches explained here try to represent complex 3D meshes as compactly as possible by using point-based geometry or a NPR(Non-photorealistic rendering) technique.

 Point-based geometry has been evolved as an alternative to the use of intermediate triangle-based representations.  Their major advantage is that point sets do not require any connectivity information, and no topological consistency conditions have to be satisfied. Moreover these schemes involve a hierarchical data structure which allows a LOD(Level of Detail) technique.

 Non-photorealistic rendering can convey shape and meaning with a minimum of visual distraction. In particular, a good line-drawing can give us a lot of information about the geometry of a shape.  Line-drawing is often combined with other styles, but remains fundamental because of its power to convey important features.

 
 
2. Technical Issues

 A. Polygonal Surface Reconstruction from Point Clouds


Fig A-1. Algorithm overview of polygonal surface reconstruction from point clouds.

 This research proposes a new procedure for generating a polygonal surface for approximating arbitrary point cloud data with any topology.  Our intent is to directly reconstruct a subdivision surface from unorganized points.  The subdivision surface is well known to be a natural level-of-detail and memory-efficient representation of the polygonal surface.  Because we use the interpolatory subdivision method like a modified butterfly scheme, we don't need to sample the displacement of all vertices but just those of odd vertices for each level.  So displaced butterfly subdivision surfaces (DBSS) have more efficiency of memory than the ones by a Loop scheme.

 
left: point clouds, right: shrink-wrapped rough mesh

 
DBSS representation

Fig A-2. Polygonal surface reconstruction from a Chinese scimitar model.


 B. 3D Feature Detection



Fig B-1. Algorithm overview of 3D feature detection.

 In the first step of this approach, we extract lines from object-space normal vectors.  We do not use shading information, since this represents a reduced level of geometric information.  However, normal vectors alone are not adequate when the surface changes abruptly which happens at the outlines of objects.  Therefore we also use a depth discontinuity filter.  The object-space normal vectors and depth information acquired in this first step are packed into textures.

 In second step we apply edge detection to the textures, which yields lines.  Laplacian edge detection produces good results and is relatively easy to implement on modern graphics hardware.  We also use a simple Sobel filter to identify outlines reliably.

 
left: target model, right: our method

 
left: apparent ridges, right: suggestive contours

Fig B-2. Dragon model rendered with 3 different techniques.



Fig B-3. Toon shading application for various models.


 
 
3. Related Publication

[1] Jung Lee, Sun-Jeong Kim and Chang-Hun Kim, "Remeshing Visual Hull Approximation by Displaced Butterfly Subdivision Surfaces," Applied Mathematics & Information Sciences, 2012.

[2] Jung Lee, Sun-Jeong Kim and Chang-Hun Kim, "Displaced Butterfly Subdivision Surface Reconstruction from Point Clouds Using MLS Approximation," Advanced Science Letters, 2012.

[3] Soo-Kyun Kim, Jung Lee and Chang Hun Kim, "Clearer Line-Drawings of 3D Models Rendered More Quickly on a GPU," Journal of Information, Volume 14, Number 7, pp. 2243-2250, October 2011.


ABOUT_US  |  RESEARCH  |  PUBLICATION  |  COURSE  |  SEMINAR  |  LINKS