Visual Hull-Based Geometric Data Compression of a 3-D Object

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 168
  • Download : 0
As image-based 3-D modeling is used in a variety of applications, accordingly, the compression of 3-D object geometry represented by multiple images becomes an important task. This paper presents a model-based approach to predict the geometric structure of an object using its visual hull. A visual hull is a geometric entity generated by shape-from-silhouette (SFS), and consequently it largely follows the overall shape of the object. The construction of a visual hull is computationally inexpensive and a visual hull can be encoded with relatively small amount of bits because it can be represented with 2-D silhouette images. Therefore, when it comes to the predictive compression of object's geometric data, the visual hull should be an effective predictor. In the proposed method, the geometric structure of an object is represented by a layered depth image (LDI), and a visual hull from the LDI data is computed via silhouette generation and SFS. The geometry of an object is predicted with the computed visual hull, and the visual hull data with its prediction errors are encoded. Simulation results show that the proposed predictive coding based on visual hull outperforms the previous image-based methods and the partial surface-based method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-07
Language
English
Article Type
Article
Keywords

LAYERED DEPTH IMAGE; REPRESENTATION; ALGORITHM

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.25, no.7, pp.1151 - 1160

ISSN
1051-8215
DOI
10.1109/TCSVT.2014.2361420
URI
http://hdl.handle.net/10203/200687
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0