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Interactive Collision Detection for Deformable Models Using Streaming AABBs

Xinyu Zhang, Y.J. Kim
Dept. of Comput. Sci. & Eng., Ewha Woman’s Univ., Seoul
IEEE Transactions on Visualization and Computer Graphics, 2007

@article{zhang2007interactive,

   title={Interactive collision detection for deformable models using streaming AABBs},

   author={Zhang, X. and Kim, Y.J.},

   journal={IEEE Transactions on Visualization and Computer Graphics},

   pages={318–329},

   year={2007},

   publisher={Published by the IEEE Computer Society}

}

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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 overtime, 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 and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm and o–bserved about two times improvement
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