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BADF: Bounding Volume Hierarchies Centric Adaptive Distance Field Computation for Deformable Objects on GPUs.

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Title: BADF: Bounding Volume Hierarchies Centric Adaptive Distance Field Computation for Deformable Objects on GPUs.
Authors: Chen, Xiao-Rui1 (AUTHOR) chenxiaorui@zju.edu.cn, Tang, Min1 (AUTHOR), Li, Cheng1 (AUTHOR), Manocha, Dinesh2 (AUTHOR), Tong, Ruo-Feng1 (AUTHOR)
Source: Journal of Computer Science & Technology (10009000). Jun2022, Vol. 37 Issue 3, p731-740. 10p.
Subjects: NVIDIA Corp., Central processing units, Graphics processing units
Abstract: We present a novel algorithm BADF (Bounding Volume Hierarchy Based Adaptive Distance Fields) for accelerating the construction of ADFs (adaptive distance fields) of rigid and deformable models on graphics processing units. Our approach is based on constructing a bounding volume hierarchy (BVH) and we use that hierarchy to generate an octree-based ADF. We exploit the coherence between successive frames and sort the grid points of the octree to accelerate the computation. Our approach is applicable to rigid and deformable models. Our GPU-based (graphics processing unit based) algorithm is about 20x–50x faster than current mainstream central processing unit based algorithms. Our BADF algorithm can construct the distance fields for deformable models with 60k triangles at interactive rates on an NVIDIA GTX GeForce 1060. Moreover, we observe 3x speedup over prior GPU-based ADF algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: BADF: Bounding Volume Hierarchies Centric Adaptive Distance Field Computation for Deformable Objects on GPUs.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Xiao-Rui%22">Chen, Xiao-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chenxiaorui@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Min%22">Tang, Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Cheng%22">Li, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Manocha%2C+Dinesh%22">Manocha, Dinesh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tong%2C+Ruo-Feng%22">Tong, Ruo-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22NVIDIA+Corp%2E%22">NVIDIA Corp.</searchLink><br /><searchLink fieldCode="DE" term="%22Central+processing+units%22">Central processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink>
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  Data: We present a novel algorithm BADF (Bounding Volume Hierarchy Based Adaptive Distance Fields) for accelerating the construction of ADFs (adaptive distance fields) of rigid and deformable models on graphics processing units. Our approach is based on constructing a bounding volume hierarchy (BVH) and we use that hierarchy to generate an octree-based ADF. We exploit the coherence between successive frames and sort the grid points of the octree to accelerate the computation. Our approach is applicable to rigid and deformable models. Our GPU-based (graphics processing unit based) algorithm is about 20x–50x faster than current mainstream central processing unit based algorithms. Our BADF algorithm can construct the distance fields for deformable models with 60k triangles at interactive rates on an NVIDIA GTX GeForce 1060. Moreover, we observe 3x speedup over prior GPU-based ADF algorithms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11390-022-0331-x
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        Text: English
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              M: 06
              Text: Jun2022
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