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Hierarchical Point Distance Fields

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Advances in Visual Computing (ISVC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13018))

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Abstract

We present an extension to bounding volume hierarchies for fast approximate distance queries on arbitrary objects for which a distance bound is known. Using points on the objects as examples, we compute the relative error of approximating all objects in a node with the node boundary. This scheme can be applied efficiently to the hierarchical structure. We show that both distance queries as well as algorithms relying on those queries are significantly sped up on the CPU and GPU by our algorithm.

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References

  1. de Assis Zampirolli, F., Filipe, L.: A fast CUDA-based implementation for the euclidean distance transform. In: 2017 International Conference on High Performance Computing & Simulation (HPCS), pp. 815–818. IEEE (2017). https://doi.org/10.1109/HPCS.2017.123, https://ieeexplore.ieee.org/document/8035162/

  2. Baerentzen, J., Aanaes, H.: Signed distance computation using the angle weighted pseudonormal. IEEE Trans. Vis. Comput. Graph 11(3), 243–253 (2005). https://doi.org/10.1109/TVCG.2005.49, http://ieeexplore.ieee.org/document/1407857/

  3. Barill, G., Dickson, N.G., Schmidt, R., Levin, D.I.W., Jacobson, A.: Fast winding numbers for soups and clouds. ACM Trans. Graph. 37(4), 1–12 (2018). https://doi.org/10.1145/3197517.3201337, http://dl.acm.org/citation.cfm?doid=3197517.3201337

  4. Bastos, T., Celes, W.: GPU-accelerated adaptively sampled distance fields. In: 2008 IEEE International Conference on Shape Modeling and Applications, pp. 171–178. IEEE (2008). https://doi.org/10.1109/SMI.2008.4547967, http://ieeexplore.ieee.org/document/4547967/

  5. Bálint, C., Valasek, G.: Accelerating Sphere Tracing. In: Diamanti, O., Vaxman, A. (eds.) EG 2018 - Short Papers. The Eurographics Association (2018). https://doi.org/10.2312/egs.20181037

  6. Fabbri, R., Costa, L.D.F., Torelli, J.C., Bruno, O.M.: 2d euclidean distance transform algorithms: a comparative survey. ACM Comput. Surv. 40(1) (2008). https://doi.org/10.1145/1322432.1322434

  7. Frisken, S.F., Perry, R.N., Rockwood, A.P., Jones, T.R.: Adaptively sampled distance fields: a general representation of shape for computer graphics. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’00, pp. 249–254. ACM Press/Addison-Wesley Publishing Co., USA (2000). https://doi.org/10.1145/344779.344899

  8. Hart, J.C.: Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. Vis. Comput. 12(10), 527–545 (1996). https://doi.org/10.1007/s003710050084, http://link.springer.com/10.1007/s003710050084

  9. Huang, J., Li, Y., Crawfis, R., Lu, S.C., Liou, S.Y.: A complete distance field representation. In: Proceedings Visualization, 2001, VIS ’01, pp. 247–561. IEEE (2001). https://doi.org/10.1109/VISUAL.2001.964518, http://ieeexplore.ieee.org/document/964518/

  10. Koschier, D., Deul, C., Bender, J.: Hierarchical hp-adaptive signed distance fields. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’16, pp. 189–198. Eurographics Association, Goslar, DEU (2016)

    Google Scholar 

  11. Krayer, B., Müller, S.: Generating signed distance fields on the GPU with ray maps. Vis. Comput. 35(6-8), 961–971 (2019). https://doi.org/10.1007/s00371-019-01683-w, http://link.springer.com/10.1007/s00371-019-01683-w

  12. Macklin, M., Müller, M., Chentanez, N., Kim, T.Y.: Unified particle physics for real-time applications. ACM Trans. Graph. 33(4) (2014). https://doi.org/10.1145/2601097.2601152

  13. Manduhu, M., Jones, M.W.: A work efficient parallel algorithm for exact euclidean distance transform. IEEE Trans. Image Process. 28(11), 5322–5335 (2019). https://doi.org/10.1109/TIP.2019.2916741, https://ieeexplore.ieee.org/document/8718507/

  14. McGuire, M.: Computer graphics archive (2017). https://casual-effects.com/data

  15. Quan, C., Stamm, B.: Mathematical analysis and calculation of molecular surfaces. J. Comput. Phys. 322(C), 760–782 (2016). https://doi.org/10.1016/j.jcp.2016.07.007

  16. Sawhney, R., Crane, K.: Monte carlo geometry processing: a grid-free approach to pde-based methods on volumetric domains. ACM Trans. Graph. 39(4) (2020). https://doi.org/10.1145/3386569.3392374

  17. Seyb, D., Jacobson, A., Nowrouzezahrai, D., Jarosz, W.: Non-linear sphere tracing for rendering deformed signed distance fields. ACM Trans. Graph. 38(6), 1–12 (2019). https://doi.org/10.1145/3355089.3356502, https://dl.acm.org/doi/10.1145/3355089.3356502

  18. Song, C., Pang, Z., Jing, X., Xiao, C.: Distance field guided \(l_1\) -median skeleton extraction. Vis. Comput. 34(2), 243–255 (2018). https://doi.org/10.1007/s00371-016-1331-z, http://link.springer.com/10.1007/s00371-016-1331-z

  19. Teschner, M.: Collision detection for deformable objects. Comput. Graph. Forum 24(1), 61–81 (2005). https://doi.org/10.1111/j.1467-8659.2005.00829.x

    Article  Google Scholar 

  20. Xu, H., Barbic, J.: 6-DoF haptic rendering using continuous collision detection between points and signed distance fields. IEEE Trans. Haptics 10(2), 151–161 (2017). https://doi.org/10.1109/TOH.2016.2613872

    Article  Google Scholar 

  21. Ytterlid, R., Shellshear, E.: Bvh split strategies for fast distance queries. J. Comput. Graph. Tech. (JCGT) 4(1), 1–25 (2015). http://jcgt.org/published/0004/01/01/

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Author information

Authors and Affiliations

  1. University Koblenz-Landau, Koblenz, Germany

    Bastian Krayer & Stefan Müller

Authors
  1. Bastian Krayer
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  2. Stefan Müller
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Corresponding author

Correspondence to Bastian Krayer .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

  3. University of South Carolina, Columbia, SC, USA

    Tong Yan

  4. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  5. School of Engineering and Computing, University of Durham, Durham, Durham, UK

    Frederick Li

  6. Airbnb, New York, NY, USA

    Conglei Shi

  7. Peking University, Beijing, China

    Xiaoru Yuan

  8. Purdue University, West Lafayette, IN, USA

    Christos Mousas

  9. IST, School of Modeling, Simulation, and Training, Orlando, FL, USA

    Gerd Bruder

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Krayer, B., Müller, S. (2021). Hierarchical Point Distance Fields. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_35

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  • DOI: https://doi.org/10.1007/978-3-030-90436-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90435-7

  • Online ISBN: 978-3-030-90436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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