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Interactive collision detection for deformable models using streaming AABBs

Title
Interactive collision detection for deformable models using streaming AABBs
Authors
Zhang X.Kim Y.J.
Ewha Authors
김영준Xinyu Zhang
SCOPUS Author ID
김영준scopus
Issue Date
2007
Journal Title
IEEE Transactions on Visualization and Computer Graphics
ISSN
1077-2626JCR Link
Citation
vol. 13, no. 2, pp. 318 - 329
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At runtime, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30 - 100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm [2] and observed about two times improvement. © 2007 IEEE.
DOI
10.1109/TVCG.2007.42
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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