Abstract
Grass is a very important element of nature and it could almost be found in every natural scene. Thus grass modeling, rendering as well as simulation becomes an important task for virtual scene creation. Existing manual grass modeling and reconstruction methods have researched on generate or reconstructing plants. However, these methods do not achieve a good result for grass blades for their extremely thin shape and almost invariant surface color. Besides, current simulation and rendering methods for grasses suffer from efficiency and computation complexity problems. This paper introduces a framework that reconstructs the grass blade model from the color-enhanced depth map, simplifies the grass blade model and achieves extremely large scale grassland simulation with individual grass blade response. Our method starts with reconstructing the grass blade model. We use color information to guide the refinement of captured depth maps from cameras based on an autoregressive model. After refinement, a high-quality depth map is used to reconstruct thin blade models, which cannot be well handled by multi-view stereo methods. Then we introduce a blade simplification method according to each vertex’s movement similarity. This method takes both geometry and movement characteristics of grass into account when simplifying blade mesh. In addition, we introduce a simulation technique for extremely large grassland that achieve tile management on GPU and allow individual response for each grass blade. Our method excels at reconstructing slender grass blades as well as other similar plants, and realistic dynamic simulation for large scale grassland.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bakay, B., Lalonde, P., Heidrich, W.: Real-time animated grass. In: Eurographics 2002 (2002)
Bommes, D., et al.: Quad-mesh generation and processing: a survey, vol. 32(6), pp. 51–76 (2013)
Boulanger, K., Pattanaik, S.N., Bouatouch, K.: Rendering grass in real time with dynamic lighting. IEEE Comput. Graphics Appl. 29(1), 32–41 (2009)
Bradley, D., Nowrouzezahrai, D., Beardsley, P.: Image-based reconstruction and synthesis of dense foliage. ACM Trans. Graph. (TOG) 32(4), 74 (2013)
Chen, K., Johan, H.: Real-time continuum grass. In: 2010 IEEE Virtual Reality Conference (VR), pp. 227–234. IEEE (2010)
Fan, Z., Li, H., Hillesland, K., Sheng, B.: Simulation and rendering for millions of grass blades. In: Proceedings of the 19th Symposium on Interactive 3D Graphics and Games, pp. 55–60. ACM (2015)
Funkhouser, T.A., Séquin, C.H.: Adaptive display algorithm for interactive frame rates during visualization of complex virtual environments. In: Proceedings of the 20th Annual Conference on Computer graphics and Interactive Techniques, pp. 247–254. ACM (1993)
Guerraz, S., Perbet, F., Raulo, D., Faure, F., Cani, M.P.: A procedural approach to animate interactive natural sceneries. In: 16th International Conference on Computer Animation and Social Agents, pp. 73–78. IEEE (2003)
Han, D., Harada, T.: Real-time hair simulation with efficient hair style preservation. In: Proceedings of the VRIPHYS 2012, pp. 45–51 (2012)
Kajiya, J.T., Kay, T.L.: Rendering fur with three dimensional textures. ACM Siggraph Comput. Graph. 23(3), 271–280 (1989)
Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., Feng, D.D.: Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Trans. Syst. Man Cybern. Syst. 49(9), 1806–1819 (2019)
Karambakhsh, A., Kamel, A., Sheng, B., Li, P., Yang, P., Feng, D.D.: Deep gesture interaction for augmented anatomy learning. Int. J. Inf. Manage. 45, 328–336 (2019)
Lu, P., Sheng, B., Luo, S., Jia, X., Wu, W.: Image-based non-photorealistic rendering for realtime virtual sculpting. Multimedia Tools Appl. 74(21), 9697–9714 (2014). https://doi.org/10.1007/s11042-014-2146-4
Mündermann, L., MacMurchy, P., Pivovarov, J., Prusinkiewicz, P.: Modeling lobed leaves. In: Proceedings Computer Graphics International 2003, pp. 60–65. IEEE (2003)
Neyret, F.: Synthesizing verdant landscapes using volumetric textures. In: Pueyo, X., Schröder, P. (eds.) EGSR 1996. E, pp. 215–224. Springer, Vienna (1996). https://doi.org/10.1007/978-3-7091-7484-5_22
Perlin, K.: An image synthesizer. ACM Siggraph Comput. Graph. 19(3), 287–296 (1985)
Qiu, H., Chen, L., Chen, J.X., Liu, Y.: Dynamic simulation of grass field swaying in wind. J. Softw. 7(2), 431–439 (2012)
Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., Kang, S.B.: Image-based plant modeling, vol. 25(3), pp. 599–604 (2006)
Ray, N., Li, W.C., Lévy, B., Sheffer, A., Alliez, P.: Periodic global parameterization. ACM Trans. Graph. (TOG) 25(4), 1460–1485 (2006)
Reeves, W.T., Blau, R.: Approximate and probabilistic algorithms for shading and rendering structured particle systems. ACM SIGGRAPH Comput. Graph. 19(3), 313–322 (1985)
Shah, M.A., Kontinnen, J., Pattanaik, S.: Real-time rendering of realistic-looking grass. In: Proceedings of the 3rd International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia, pp. 77–82. ACM (2005)
Sheng, B., Li, P., Zhang, Y., Mao, L.: GreenSea: visual soccer analysis using broad learning system. IEEE Trans. Cybern. 1–15 (2020)
Sousa, T.: Vegetation procedural animation and shading in crysis. GPU Gems 3, 373–385 (2007)
Tan, P., Zeng, G., Wang, J., Kang, S.B., Quan, L.: Image-based tree modeling. ACM Trans. Graph. 26(3), 87 (2007)
Wang, C., Wang, Z., Zhou, Q., Song, C., Guan, Y., Peng, Q.: Dynamic modeling and rendering of grass wagging in wind. Comput. Anim. Virtual Worlds 16(3–4), 377–389 (2005)
Wikipedia: Hausdorff distance, Wikipedia, the free encyclopedia (2015). Accessed 16 July 2015
Acknowledgement
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFF0300903, in part by the National Natural Science Foundation of China under Grant 61872241 and Grant 61572316, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 15490503200, Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, S. et al. (2020). GPU-based Grass Simulation with Accurate Blade Reconstruction. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-61864-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61863-6
Online ISBN: 978-3-030-61864-3
eBook Packages: Computer ScienceComputer Science (R0)