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研究生: 江冠儀
Chiang, Kuan-Yi
論文名稱: 使用OpenCL實現蒙地卡羅路徑追蹤於Android手機平台
OpenCL Monte Carlo Path Tracing On Android
指導教授: 張鈞法
Chang, Chun-Fa
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: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 33
中文關鍵詞: 圖形處理器光線追蹤異質性系統架構平行運算AndroidOpenCL
論文種類: 學術論文
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  • 隨著科技的發展手機的進步速度越趨明顯,手機遊戲也變成開發手機產品中一個不可或缺的角色。隨著手機晶片上的運算能力快速進步,讓手機遊戲可以從2D快速進入到3D的手機遊戲時代,而相對於畫面品質和效能也是越來越苛求。為了使3D場景的繪製達到更逼真的畫質,至今已經發展了許多不同的演算法以模擬真實環境的光照情形,其中以光線追蹤方法擁有物理運算的正確性並能夠直覺性地計算光照效果,但因光線追蹤的運算速度相對於其他手機遊戲渲染效果是較慢的所以幾乎沒有人將其運用於手機遊戲當中。
    本研究搭配OpenCL異質系統架構中平行且良好地運作之特性,對於光線追蹤龐大的計算下平行處理能讓畫面的渲染速度更有效率且畫面品質將有不錯的效果。本渲染對象以動態場景為主,會分別對於場景的複雜度和其渲染速度上做效能分析,將重點著重於可行性的探討與提升程式效能,針對所使用的平台進行實驗結果比較以及影響效能的相關探討,最後,我們將因應平台特性將演算法與之結合,試圖找出在多核心平行化架構下提升各平台程式效能的實驗依據並以其研究出適合於手機平台上的光線追蹤演算法架構。

    With development of mobile chip computing technique increasingly, it enhances the growth of a range of related to frame quality especially mobile game. It progresses the mobile game transform from two-dimensional space generation to three-dimensional (3D) space game. Graphics Processing Unit (GPU) of mobile phone offers powerful computing ability, it is applied to 3D scene compute. Majority of 3D game scene are not making user to experience realistic. In order to improve 3D sense realistic, that developed a number of different algorithms to compute ray in the scene. This research focus on ray tracing algorithm. Ray tracing uses mathematical formulas, it can calculate lighting realistically and correctly. However ray tracing need a lot of compute, it spends too much time to render. If ray tracing compare with others mobile’s render technique, it will too slowly.
    This research uses OpenCL to accelerate ray tracing in mobile phone. It will make scene not only realistically but also efficiently. We will use other platform to experiment our research, and find out good ray tracing algorithm for mobile phone.

    摘 要 ii ABSTRACT iii 誌 謝 iv 附表目錄 vii 附圖目錄 viii 第一章 簡介 1 1.1 研究背景 1 1.2 研究目的 2 1.3 論文架構 2 第二章 相關研究探討 4 2.1 Ray Tracing 4 2.2 Android NDK 5 2.3 OpenCL Platform 6 2.3.1 Execution Model 7 2.3.2 Memory Model 7 2.4 參考相關研究 8 第三章 演算法設計與討論 9 3.1 Monte Carlo Path Tracer 10 3.2 K-D Tree 10 3.2.1 K-D Tree Construction 11 3.3.2 K-D Tree Traversal 12 3.3程式安裝說明 13 3.4 Android JNI 15 第四章 實驗結果分析與設置 17 4.1實驗平台分析 17 4.2 三角片場景測試結果 18 4.3 K-D Tree實驗結果分析 19 4.4 Cross Platform實驗數據分析 21 第五章 結論與未來展望 29 5.1結論 29 5.2未來展望 30 參考文獻 31 參考資料 32 附錄 33

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