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研究生: 潘慶
Pan, Ching
論文名稱: 光譜算圖之進階採樣方法
Advanced Sampling Strategies of Spectral Rendering
指導教授: 張鈞法
Chang, Chun-Fa
口試委員: 張鈞法
Chang, Chun-Fa
賴祐吉
Lai, Yu-Chi
陳英傑
Chen, Ying-Chieh
陳履恆
Chen, Lieu-Hen
口試日期: 2023/06/20
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 26
中文關鍵詞: 算圖光譜光線追蹤色散
英文關鍵詞: Rendering, Spectral, Ray Tracing, Dispersion
研究方法: 實驗設計法比較研究
DOI URL: http://doi.org/10.6345/NTNU202300792
論文種類: 學術論文
相關次數: 點閱:68下載:6
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  • 隨著科技的發展,無論在遊戲或是模擬或是室內設計上,算出一張擁有正確光影特效的圖成為一件非常重要的事情。傳統上使用如path tracing等等可以收斂出正確結果的演算法往往會需要大量的時間。隨著GPU性能的增加,人們認為在不久的將來就可以見到遊戲等應用使用相關的技巧做及時的計算。然而,傳統的演算法如path tracing並沒有考慮到光的頻率波長等等,因此傳統上使用這類的方法無法得到包含色散等等的結果。雖然將演算法調整以支援波長的採樣並不是一件難事,然而,若是加入了波長相關的計算會因為多一個維度採樣的原因使得整體結果更加難以收斂。本篇論文的主要目標便是透過各種採樣的技巧嘗試降低波長採樣帶來的收斂減速效果,使得我們可以在一個合理的時間內得到一張包含色散效果的相對沒有太多雜訊的圖。

    With the rapid development of technology, it has been an important thing to generate a photorealistic image with correct lighting effect whether in games, simulations or interior design. Traditionally, we often uses algorithms such as path tracing in order to generate a photorealistic image. However, it often takes a lot of time in order to generate a converged image without noise. As the performance of GPU increases year by year, it is believed that we can see real time unbiased photorealistic rendering used in applications like games in near future. However, Traditional algorithms such as path tracing didn't take into account the frequency or wavelength of light. As a result, images generated by these methods would not include lighting effects such as dispersion. Although it is not a big problem to adjust the algorithm to consider wavelength, it would take a lot more time to converge because of the additional dimension involved in the integration. The main goal of this paper is to increase the speed of convergence when we add wavelength sampling using various method. This makes us generate a converged image with dispersion effect without too much noise in a much more reasonable time.

    第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 論文架構 3 第二章 文獻探討 4 2.1 Path Tracing 4 2.1.1 Path Integral 4 2.1.2 Monte Carlo Integration 5 2.1.3 Monte Carlo Path Tracing 5 2.2 Multiple Importance Sampling 6 2.3 Bidirectional Methods 6 2.3.1 Bidirectional Path Tracing 7 2.3.2 Combinatorial Bidirectional Path Tracing 7 2.3.3 Probabilistic Connections for Bidirectional Path Tracing 8 2.4 Wavelength Sampling 8 2.4.1 Hero Wavelength Sampling 9 第三章 研究方法設計 10 3.1 路徑分類法 10 3.1.1 Full Wavelength 11 3.1.2 Single Wavelength 11 3.1.3 Connection 11 3.1.3.1 Full Wavelength and Full Wavelength 12 3.1.3.2 Full Wavelength and Single Wavelength 13 3.1.3.3 Single Wavelength and Single Wavelength 13 3.1.4 路徑分類法的優勢 14 3.2 邊界搜索法 14 3.2.1 波長的邊界 15 3.2.2 邊界的採樣 15 3.2.3 邊界的搜索 17 3.2.3.1 邊界擴張法 18 3.2.3.2 邊界二分搜 19 3.2.3.3 提升採樣效率 20 第四章 實驗結果與討論 21 4.1 實驗說明 21 4.1.1 實驗環境 21 4.2 實驗結果 22 4.2.1 路徑分類法 22 4.2.2 邊界搜索法 23 4.3 結論 25 參考文獻 26

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