Study of surface representation methods based on signed distance functions

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

The paper studies surface rendering methods based on ray tracing for representations based on signed distance functions. The main objects of interest were the rendering algorithm execution time, the amount of memory occupied, and the accuracy of the surface representation estimated by the render using the PSNR metric. Six different representations and four intersection search algorithms were analyzed. In all cases, a bounding volume hierarchy was used as an accelerating structure. The comparison revealed promising representations and algorithms and showed that distance functions in some cases are not inferior to polygonal models in speed, while they can win in terms of memory consumption and represent the surface with a good level of accuracy.

全文:

受限制的访问

作者简介

A. Garifullin

Keldysh Institute of Applied Mathematics, Russian Academy of Sciences 4 Miusskaya Square

编辑信件的主要联系方式.
Email: albert.garifullin@gin.keldysh.ru
ORCID iD: 0000-0001-5385-4841
俄罗斯联邦, Moscow, 125047

V. Frolov

Institute of Artificial Intelligence, Moscow State University Leninskie Gory; Keldysh Institute of Applied Mathematics, Russian Academy of Sciences 4 Miusskaya Square; Moscow State University Leninskie Gory Faculty of Computational Mathematics and Cybernetics

Email: vladimir.frolov@graphics.cs.msu.ru
ORCID iD: 0000-0001-8829-9884
俄罗斯联邦, Moscow, 119899; Moscow, 125047; Moscow, 119899

A. Budak

Institute of Artificial Intelligence, Moscow State University Leninskie Gory; Moscow State University Leninskie Gory Faculty of Computational Mathematics and Cybernetics

Email: s02220347@gse.cs.msu.ru
ORCID iD: 0009-0005-6819-4184
俄罗斯联邦, Moscow, 119899; Moscow, 119899

V. Galaktionov

Keldysh Institute of Applied Mathematics, Russian Academy of Sciences

Email: vlgal@gin.keldysh.ru
ORCID iD: 0000-0003-1252-8294
俄罗斯联邦, 4 Miusskaya Square, Moscow, 125047

参考

  1. Rogers D.F. An Introduction to NURBS: With Historical Perspective, Elsevier, 2000.
  2. Sitzmann V., Martel J., Bergman A., Lindell D., Wetzstein G. Implicit neural representations with periodic activation functions, Advances in Neural Information Processing Systems, Larochelle H., Ranzato M., Hadsell R., Balcan M.F., and Lin H., Eds., Curran Associates, 2020, vol. 33, pp. 7462–7473. https://proceedings.neurips.cc/paper_files/paper/2020/file/53c04118df112c13a8c34b38343b9c10-Paper.pdf
  3. Takikawa T., Litalien J., Yin K., Kreis K., Loop Ch., Nowrouzezahrai D., Jacobson A., Mcguire M., Fidler S. Neural geometric level of detail: Real-time rendering with implicit 3d shapes, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, 2021, IEEE, 2021, pp. 11358–11367. https://doi.org/10.1109/cvpr46437.2021.01120
  4. Luan F., Zhao Sh., Bala K., Dong Zh. Unified shape and SVBRDF recovery using differentiable Monte Carlo rendering, Comput. Graphics Forum, 2021, vol. 40, no. 4, pp. 101–113. https://doi.org/10.1111/cgf.14344
  5. Nicolet B., Jacobson A., Jakob W. Large steps in inverse rendering of geometry, ACM Trans. Graphics, 2021, vol. 40, no. 6, p. 248. https://doi.org/10.1145/3478513.3480501
  6. Vicini D., Speierer S., Jakob W. Differentiable signed distance function rendering, ACM Trans. Graphics, 2022, vol. 41, no. 4, p. 125. https://doi.org/10.1145/3528223.3530139
  7. Cao Yu., Li H. DiffSDF: Learning implicit surface from noisy point clouds, 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, Australia, 2023, IEEE, 2023, pp. 197–204. https://doi.org/10.1109/DICTA60407.2023.00035
  8. Hart J.C. Sphere tracing: A geometric method for the antialiased ray tracing of implicit surfaces, Visual Comput., 1996, vol. 12, no. 10, pp. 527–545. https://doi.org/10.1007/s003710050084
  9. Quilez I. Distance functions. https://iquilezles.org/articles/distfunctions/ (сited June 12, 2024)
  10. Quilez I. Raymarching terrain. https://iquilezles.org/articles/terrainmarching/ (сited June 12, 2024)
  11. Polkowski D. San Base: Forefront of computer graphics. https://www.youtube.com/@lashenko (сited June 12, 2024)
  12. Frisken S.F., Perry R.N., Rockwood A.P., Jones T.R. Adaptively sampled distance fields: A general representation of shape for computer graphics, Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, 2000, New York: ACM Press/Addison-Wesley, 2000, pp. 249–254. https://doi.org/10.1145/344779.344899
  13. Sellers G., Kessenich J. Vulkan Programming Guide: The Official Guide to Learning Vulkan, Addison–Wesley, 2016.
  14. Koschier D., Deul C., Brand M., Bender J. An hp-adaptive discretization algorithm for signed distance field generation, IEEE Trans. Visualization Comput. Graphics, 2017, vol. 23, no. 10, pp. 2208–2221. https://doi.org/10.1109/tvcg.2017.2730202
  15. Söderlund H.H., Evans A., Akenine-Möller T. Ray tracing of signed distance function grids, Journal of Computer Graphics Techniques, 2022, vol. 11, no. 3, pp. 94–113.
  16. Fujimoto A., Iwata K. Accelerated ray tracing, Computer Graphics, Kunii T.L., Ed., Tokyo: Springer, 1985, pp. 41–65. https://doi.org/10.1007/978-4-431-68030-7_4
  17. Weier P., Rath A., Michel É., Georgiev I., Slusallek P., Boubekeur T. N-BVH: Neural ray queries with bounding volume hierarchies, Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers’24, Denver, CO, 2024, Burbano A., Zorin D., and Jarosz W., Eds., New York: Association for Computing Machinery, 2024, p. 99. https://doi.org/10.1145/3641519.3657464
  18. Fujieda S., Kao C.C., Harada T. Neural intersection function, High-Performance Graphics – Symposium Papers, Bikker J. and Gribble Ch., Eds., The Eurographics Association, 2023. https://doi.org/10.2312/hpg.20231135
  19. Galin E., Guérin E., Paris A., Peytavie A. Segment tracing using local Lipschitz bounds, Comput. Graphics Forum, 2020, vol. 39, no. 2, pp. 545–554. https://doi.org/10.1111/cgf.13951
  20. Marmitt G., Kleer A., Friedrich H., Wald I., Slusallek P. Fast and accurate ray-voxel intersection techniques for iso-surface ray tracing, Proceedings of the Vision, Modeling, and Visualization Conference (VMV 2004), Stanford, CA, 2004, vol. 4, pp. 429–435.
  21. Stich M., Friedrich H., Dietrich A. Spatial splits in bounding volume hierarchies, Proceedings of the Conference on High Performance Graphics 2009, New Orleans, 2009, Spencer S.N., McAllister D., Pharr M., and Wald I., Eds., New York: Association for Computing Machinery, 2009, pp. 7–13. https://doi.org/10.1145/1572769.1572771
  22. Wald I., Woop S., Benthin C., Johnson G.S., Ernst M. Embree: A kernel framework for efficient CPU ray tracing, ACM Trans. Graphics, 2014, vol. 33, no. 4, p. 143. https://doi.org/10.1145/2601097.2601199
  23. Frolov V., Sanzharov V., Galaktionov V. Kernel_slicer: High-level approach on top of GPU programming API, 2022. Ivannikov Ispras Open Conference (ISPRAS), Moscow, 2022, IEEE, 2022, pp. 11–17. https://doi.org/10.1109/ispras57371.2022.10076850
  24. Zhdanov D.D., Potemin I.S., Zhdanov A.D. Embree technology for ray tracing in optical systems with free shape surfaces, Trudy 33-i Mezhdunarodnoi konferentsii po komp’yuternoi grafike i mashinnomu zreniyu GrafiKon 2023 (Proceedings of the 33rd International Conference on Computer Graphics and Machine Visiton GraphiCon2023), Moscow: Institut Prikladnoi Matematiki im. M.V. Keldysha Rossiiskoi Akademii Nauk, 2023, vol. 33, pp. 97–107. https://doi.org/10.20948/graphicon-2023-97-107
  25. Jones M.W. Distance field compression, J. WSCG, 2004, vol. 12, no. 2, pp. 199–204.
  26. Nießner M., Zollhöfer M., Izadi Sh., Stamminger M. Real-time 3D reconstruction at scale using voxel hashing, ACM Trans. Graphics, 2013, vol. 32, no. 6, p. 169. https://doi.org/10.1145/2508363.2508374
  27. Boyko A.I., Matrosov M.P., Oseledets I.V., Tsetserukou D., Ferrer G. TT-TSDF: Memory-efficient TSDF with low-rank tensor train decomposition, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, 2020, IEEE, 2020, pp. 10116–10121. https://doi.org/10.1109/iros45743.2020.9341464
  28. Garland M., Heckbert P.S. Surface simplification using quadric error metrics, Seminal Graphics Papers: Pushing the Boundaries, Volume 2, Whitton M.C., Ed., New York: Association for Computing Machinery, 2023, vol. 2, p. 15. https://doi.org/10.1145/3596711.3596727

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Schematic representation of the SDF representations considered in the paper. From left to right: regular grid, octree, octree with preserving values in corners (frame_octree), sparse voxel set (SVS), sparse brick set (SBS). The upper diagrams demonstrate the partitioning of the space for each of the representations. The lower diagrams show the leaf nodes of the BVH for each of the representations. Green and red dots are the positions from which the SDF is preserved in this representation. Green ones are outside the object, red ones are inside. The lower diagrams show non-empty (blue) leaf nodes of the BVH for each of the representations.

下载 (1MB)
索引源数据
3. Fig. 2. Test models.

下载 (1MB)
索引源数据
4. Fig. 3. The original 3D model (left) and Sparse Voxel Set (sdf_SVS) of different sizes built from it.

下载 (3MB)
索引源数据
5. Fig. 4. Comparison of SDS with compact surface representation methods (decimation, NGLOD and N-BVH). In the graphs, the x-axis represents the size, and the y-axis represents the PSNR metric value.

下载 (3MB)
索引源数据

版权所有 © Russian Academy of Sciences, 2025