Paper Info
Title
VIV: Using visible internal volume to compute junction-aware shape descriptor of 3D articulated models.
Abstract
An articulated shape is composed of a set of rigid parts connected by some flexible junctions. Junctions have been demonstrated to be critical local features in many visual tasks such as feature recognition, segmentation, matching, motion tracking and functional prediction. However, efficient description and detection of junctions still remain a research challenge due to the high complexity of articulated deformation. This paper presents a novel junction-aware shape descriptor for a 3D articulated model defined by a closed manifold surface. To encode junction information on the shape boundary, the core idea is to develop a new geometric measure, called the visible internal volume (VIV) function, which associates the shape's volumetric context to its boundary surface. The VIV at an arbitrary point on the shape boundary is defined as the volume of visible region within the shape as observed from the point. The VIV variation serves as the new shape descriptor. One advantage of using the VIV for 3D articulated shape description is that it is robust to articulation and it reflects the shape structure and deformation well without any explicit shape decomposition or prior skeleton extraction procedure. The experimental results and several potential applications are presented for demonstrating the effectiveness of our method.
Year
DOI
Venue
2016
10.1016/j.neucom.2015.06.115
Neurocomputing
Keywords
Field
DocType
3D articulated shapes,Junction detection,Shape descriptor,Visible internal volume (VIV),Visibility graph
Active shape model,Topology,Visibility graph,Closed manifold,Segmentation,Feature recognition,Deformation (mechanics),Match moving,Mathematics,Shape analysis (digital geometry)
Journal
Volume
ISSN
Citations 
215
0925-2312
1
PageRank 
References 
Authors
0.35
32
4
Name
Order
Citations
PageRank
Yu-shen Liu131923.20
Hongchen Deng210.35
Min Liu311.02
Lianjie Gong430.70