請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92836完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳炳宇 | zh_TW |
| dc.contributor.advisor | Bing-Yu Chen | en |
| dc.contributor.author | 涂家銘 | zh_TW |
| dc.contributor.author | Chia-Ming Tu | en |
| dc.date.accessioned | 2024-07-02T16:12:59Z | - |
| dc.date.available | 2024-07-03 | - |
| dc.date.copyright | 2024-07-02 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-06-11 | - |
| dc.identifier.citation | [1] C. Schied, A. Kaplanyan, C. Wyman, A. Patney, C. R. A. Chaitanya, J. Burgess, S. Liu, C. Dachsbacher, A. Lefohn, and M. Salvi, “Spatiotemporal variance-guided filtering: real-time reconstruction for path-traced globa illumination,” in Proceedings of High Performance Graphics, ser. HPG ’17. New York, NY, USA: Association for Computing Machinery, 2017.
[2] H. Dammertz, D. Sewtz, J. Hanika, and H. P. A. Lensch, “Edge-avoiding `Atrous wavelet transform for fast global illumination filtering,” in Proceedings of the Conference on High Performance Graphics, ser. HPG ’10. Goslar, DEU: Eurographics Association, 2010, p. 67–75. [3] C. Schied, C. Peters, and C. Dachsbacher, “Gradient estimation for real-time adaptive temporal filtering,” Proc. ACM Comput. Graph. Interact. Tech., vol. 1, no. 2, aug 2018. [4] B. Bitterli, C. Wyman, M. Pharr, P. Shirley, A. Lefohn, and W. Jarosz, “Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting,” ACM Trans. Graph., vol. 39, no. 4, aug 2020. [5] G. Boiss ́e, “World-space spatiotemporal reservoir reuse for ray-traced global illumination,” in SIGGRAPH Asia 2021 Technical Communications, ser. SA ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3478512.3488613 [6] Y. Ouyang, S. Liu, M. Kettunen, M. Pharr, and J. Pantaleoni, “Restir gi: Path resampling for real-time path tracing,” Computer Graphics Forum, vol. 40, no. 8, pp. 17–29, 2021. [7] D. Lin, C. Wyman, and C. Yuksel, “Fast volume rendering with spatiotemporal reservoir resampling,” ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2021), vol. 40, no. 6, pp. 278:1–278:18, 12 2021. http://doi.acm.org/10.1145/3478513.3480499 [8] D. Lin, M. Kettunen, B. Bitterli, J. Pantaleoni, C. Yuksel, and C. Wyman, “Generalized resampled importance sampling: foundations of restir,” ACM Trans. Graph., vol. 41, no. 4, jul 2022. [9] A. Kuznetsov, N. K. Kalantari, and R. Ramamoorthi, “Deep adaptive sampling for low sample count rendering,” Computer Graphics Forum, vol. 37, pp. 35–44, 2018. [10] C. R. A. Chaitanya, A. S. Kaplanyan, C. Schied, M. Salvi, A. Lefohn, D. Nowrouzezahrai, and T. Aila, “Interactive reconstruction of monte carloimage sequences using a recurrent denoising autoencoder,” ACM Trans. Graph., vol. 36, no. 4, jul 2017. https://doi.org/10.1145/3072959.3073601 [11] J. Hasselgren, J. Munkberg, M. Salvi, A. Patney, and A. Lefohn, “Neural temporal adaptive sampling and denoising,” Computer Graphics Forum, vol. 39, no. 2, pp. 147–155, 2020. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13919 [12] M. Weier, T. Roth, E. Kruijff, A. Hinkenjann, A. P ́erard-Gayot, P. Slusallek, and Y. Li, “Foveated real-time ray tracing for head-mounted displays,” Comput. Graph. Forum, vol. 35, no. 7, p. 289–298, oct 2016. [13] Y. Kim, Y. Ko, and I. Ihm, “Selective foveated ray tracing for head-mounted displays,” in 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2021, pp. 413–421. [14] B. Jin, I. Ihm, B. Chang, C. Park, W. Lee, and S. Jung, “Selective and adaptive supersampling for real-time ray tracing,” in Proceedings of the Conference on High Performance Graphics 2009, ser. HPG ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 117–125. [15] L. Wang, X. Shi, and Y. Liu, “Foveated rendering: A state-of-the-art survey,” Computational Visual Media, vol. 9, no. 2, pp. 195–228, Jun 2023. [16] C. R. A. Chaitanya, A. S. Kaplanyan, C. Schied, M. Salvi, A. Lefohn, D. Nowrouzezahrai, and T. Aila, “Interactive reconstruction of monte carloimage sequences using a recurrent denoising autoencoder,” ACM Trans. Graph., vol. 36, no. 4, jul 2017. https://doi.org/10.1145/3072959.3073601 [17] J. H. Mueller, T. Neff, P. Voglreiter, M. Steinberger, and D. Schmalstieg, “Temporally adaptive shading reuse for real-time rendering and virtual reality,” vol. 40, no. 2, apr 2021. https://doi.org/10.1145/3446790 [18] L. Yang, D. Nehab, P. V. Sander, P. Sitthi-amorn, J. Lawrence, and H. Hoppe, “Amortized supersampling,” ACM Trans. Graph., vol. 28, no. 5, p. 1–12, dec 2009. https://doi.org/10.1145/1618452.1618481 [19] B. Karis, “High-qality temporal supersampling,” 2014. [20] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. [21] S. Kallweit, P. Clarberg, C. Kolb, T. Davidovi ˇc, K.-H. Yao, T. Foley, Y. He, L. Wu, L. Chen, T. Akenine-M ̈oller, C. Wyman, C. Crassin, and N. Benty, “The Falcor rendering framework,” 8 2022. https://github.com/NVIDIAGameWorks/Falcor [22] A. Lumberyard, “Amazon lumberyard bistro, open research content archive (orca),” July 2017. http://developer.nvidia.com/orca/amazon-lumberyard-bistro [23] K. A. Nicholas Hull and N. Benty, “Nvidia emerald square, open research content archive (orca),” July 2017. http://developer.nvidia.com/orca/nvidia-emerald-square [24] E. Games, “Unreal engine sun temple, open research content archive (orca),” October 2017. http://developer.nvidia.com/orca/epic-games-sun-temple 20 [25] M. Winkelmann, “Zero-day, open research content archive (orca),” November 2019. https://developer.nvidia.com/orca/beeple-zero-day [26] B. Bitterli, “Rendering resources,” 2016, https://benedikt-bitterli.me/resources/. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92836 | - |
| dc.description.abstract | 透過時空重用增加有效採樣數量對於實時光線追踪的降噪至關重要。現有的方法通常假設輸入是由固定的每像素樣本數生成的，並在時間重用當中使用指數移動平均來合併顏色。然而，當每像素樣本數變化時，這些方法無法有效利用由高樣本數生成的顏色。在這項研究中，我們提出了一種新方法，稱為「雙歷史方法」，以增強現有的時空濾波器，提升其處理可變樣本數輸入的能力。我們使用這種方法改進了變異數引導的時空濾波（SVGF）算法，並通過簡單的注視點渲染管線評估其性能。 | zh_TW |
| dc.description.abstract | Spatio-temporal reusing samples is critical to reducing noise for realtime ray-tracing. Existing methods usually assume inputs are generated by constant sample-per-pixel and use exponential moving averages to aggregate colors for temporal reuse. However, when sample-per-pixel vary, these approaches fail to effectively utilize colors generated by high sample counts. In this study, we proposed a novel method termed the "Two-history Approach" to augment existing spatio-temporal denoisers, enhancing their ability to handle inputs with variable sample count. We adapt the SVGF algorithm using our approach and evaluate its performance with a simple foveated rendering pipeline. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-02T16:12:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-02T16:12:59Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Abstract i
ListofFigures iv ListofTables v 1 Introduction 1 2 RelatedWorks 5 2.1 Spatio-temporalReuse . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 FoveatedRendering . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 TemporalGradients . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 SVGF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Two-historyApproach 11 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 FirstPhaseofBlending . . . . . . . . . . . . . . . . . . . . . . . 12 3.3 SecondPhaseofBlending . . . . . . . . . . . . . . . . . . . . . 15 3.4 CombinewithSpatialFilters . . . . . . . . . . . . . . . . . . . . 17 4 Experiments 19 4.1 ExperimentDesign . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 ImageQuality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Runtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4 MemoryUsage . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 Limitation and Future Work 30 6 Conclusion 32 Reference 33 | - |
| dc.language.iso | en | - |
| dc.subject | 電腦圖學 | zh_TW |
| dc.subject | 注視點渲染 | zh_TW |
| dc.subject | 光線追蹤 | zh_TW |
| dc.subject | Foveated Rendering | en |
| dc.subject | Computer Graphics | en |
| dc.subject | Ray-tracing | en |
| dc.title | 用於蒙地卡羅渲染的雙歷史時空濾波方法：以變異數引導的時空濾波為例 | zh_TW |
| dc.title | Two-history Approach of Spatiotemporal Filtering for Monte Carlo Rendering: Spatiotemporal Variance-Guided Filtering as Example | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 簡韶逸 | zh_TW |
| dc.contributor.coadvisor | Shao-Yi Chien | en |
| dc.contributor.oralexamcommittee | 莊永裕;張鈞法 | zh_TW |
| dc.contributor.oralexamcommittee | Yung-Yu Chuang;Chun-Fa Chang | en |
| dc.subject.keyword | 電腦圖學,光線追蹤,注視點渲染, | zh_TW |
| dc.subject.keyword | Computer Graphics,Ray-tracing,Foveated Rendering, | en |
| dc.relation.page | 36 | - |
| dc.identifier.doi | 10.6342/NTU202400987 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-06-11 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 17.06 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。