By Topic

CUDA-based Signed Distance Field Calculation for Adaptive Grids

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Taejung Park ; Dept. of Comput. Sci., Korea Univ., Seoul, South Korea ; Sung-Ho Lee ; Jong-Hyun Kim ; Chang-Hun Kim

We present a simple yet robust signed distance field (SDF) generator based on recent GPU architectures. In our approach, the squared Euclidean distance is calculated for each triangle face in parallel, and then an optimized stream reduction process is used to find the shortest distance. During this process, the stream reduction operation acts like a parallel binary space-searching routine for each level. This process uses computations and memory bandwidth efficiently because of the massive number of CUDA threads. Signs are then determined by calculating angle-weighted pseudonormals. Unlike some previous SDF approaches that only calculate the SDF near the surface or within the bounding box, our method can calculate the SDF adaptively so that there are no limitations on proximity or regularity. We also compare our GPU-based results with a kd-tree based single CPU approach for a 3D geometry synthesis application.

Published in:

Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on

Date of Conference:

June 29 2010-July 1 2010