Determining canonical views of 3D object using minimum description length criterion and compressive sensing method

Ping-Feng Chen; Hamid Krim

doi:10.1117/12.775057

26 February 2008 Determining canonical views of 3D object using minimum description length criterion and compressive sensing method

Ping-Feng Chen, Hamid Krim

Author Affiliations +

Proceedings Volume 6814, Computational Imaging VI; 68140R (2008) https://doi.org/10.1117/12.775057
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

In this paper, we propose using two methods to determine the canonical views of 3D objects: minimum description length (MDL) criterion and compressive sensing method. MDL criterion searches for the description length that achieves the balance between model accuracy and parsimony. It takes the form of the sum of a likelihood and a penalizing term, where the likelihood is in favor of model accuracy such that more views assists the description of an object, while the second term penalizes lengthy description to prevent overfitting of the model. In order to devise the likelihood term, we propose a model to represent a 3D object as the weighted sum of multiple range images, which is used in the second method to determine the canonical views as well. In compressive sensing method, an intelligent way of parsimoniously sampling an object is presented. We make direct inference from Donoho¹ and Candes'² work, and adapt it to our model. Each range image is viewed as a projection, or a sample, of a 3D model, and by using compressive sensing theory, we are able to reconstruct the object with an overwhelming probability by scarcely sensing the object in a random manner. Compressive sensing is different from traditional compressing method in the sense that the former compress things in the sampling stage while the later collects a large number of samples and then compressing mechanism is carried out thereafter. Compressive sensing scheme is particularly useful when the number of sensors are limited or the sampling machinery cost much resource or time.

Citation Download Citation

Ping-Feng Chen and Hamid Krim "Determining canonical views of 3D object using minimum description length criterion and compressive sensing method", Proc. SPIE 6814, Computational Imaging VI, 68140R (26 February 2008); https://doi.org/10.1117/12.775057

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

3D modeling

Compressed sensing

3D image processing

3D vision

3D scanning

Image fusion

Laser scanners

Show All Keywords

Keywords/Phrases

Search In:

Publication Years