在電腦圖學及電腦視覺中，“相機模型”一詞通常是指一個將三維空間對應到二維影像之透視投影轉換矩陣，此一模型在許多與透視投影幾何相關的領域中已被廣泛使用，在電腦圖學中，我們利用它將虛擬的三維場景呈像在二維的螢幕上，而在電腦視覺中，許多透視投影之幾何特性也藉由此模型推導而出，以利三維場景重構之應用。然而，此一模型無法明確地表現出與相機取樣相關之性質。相機取樣在電腦圖學的許多應用中扮演著非常重要的角色，諸如影像式繪圖（image-based rendering）與體積繪圖（volume rendering）之splatting技術、貼圖過濾（texture filtering）、多層次精細度（level of detail, LOD）之轉換評量標準、相機對場景取樣品質好壞之評量，…等等。就我們所知，由於沒有一個共通的模型來描述相機取樣，相關的研究常被個別獨立看待，許多類似的取樣性質也被個別推導而出。在這篇論文中，我們將對一些與相機取樣相關的研究主題做討論，包含一個對環場場景取樣之技術和一個用以關連並解決許多電腦圖學應用的相機取樣數學模型。
　　在電腦圖學中，利用影像式技術（image-based techniques）以築構虛擬場為一新穎之技術，相較於傳統利用多邊形來繪圖方法做比較，此類技術易於達到固定畫面率（constant frame rate）並且易於保持畫面播放之平順。在本篇論文中，我們首先提出一個對環場影像取樣的相機排列架構，稱為“偏心環場景（acentric panorama view）”，並提出在此架構下的可視範圍選取（view culling）和表列優先繪圖演算法（list-priority rendering algorithm）等的繪圖加速方法。此外，此一環場取樣方式更可拓展到可供三維遊走（walkthrough）的虛擬環境中。
　　其次，我們提出一個新的相機模型：相機取樣場（camera-sampling field）。基於相機在影像平面上為均勻之取樣，我們用向量場的數形式來表示相機取樣密度之分佈。我們提供完整的推導過程，並討論多項向量場之數學性質在相機取樣場之意義，包括通量（flux）、散度（divergence）、旋度（curl）、梯度（gradient）、等位面（level surface），…等；最後介紹相機取樣場在電腦圖學中之多項應用，包括影像式繪圖與體積繪圖之splatting技術、貼圖過濾、多層次精細度之轉換評量標準，…等，這些應用範例顯示我們所提出之相機模型可廣泛的被應用在與相機取樣分析有關之領域。

　　Conventionally, the term “camera model” in computer graphics and computer vision refers to a perspective projection matrix that maps a 3D world space onto a 2D image space. This model has worked very successfully in many domains relating directly to perspective geometry. In computer graphics, we use it to render a scene onto an image. In computer vision, it is used to derive many perspective projection geometry properties for scene reconstruction. However, this camera model does not concisely reveal the camera sampling properties. Camera sampling analysis can play an important role in many applications such as splatting for image-based rendering and volume rendering, texture filtering, level transition criterion for LOD, and sampling quality evaluation for cameras with scenes, and so on. To the best of our knowledge, because there is no general model to describe camera sampling, these works are sometimes treated independently and similar relations are derived. In this dissertation we deal with some research issues about camera sampling, including a technique to sample the panorama scenes and a mathematical model for camera sampling to relate and solve many applications in computer graphics.
　　Using image-based techniques to construct a virtual world are novel approaches in computer graphics. These approaches are easier to achieve constant frame rate and thus preserve the visual smoothness. In this dissertation, a novel 2D plenoptic function called “acentric panorama view (APV)” is first introduced to sample the panorama scenes. The rendering of an APV can be accelerated by view culling and list-priority rendering algorithm. Multiple APVs with special fields of view, 45, 60, 90, and 120, can be integrated into a larger configuration called augmented APVs, which augments the walking area in a planar walkthrough environment to form a 4D plenoptic function.
　　The second, major part in this dissertation is a new camera model called camera-sampling field. It describes the sampling density distribution of a pinhole camera according to the property that the distribution of samples on an image plane is uniform. A vector field is then used to represent this sampling density distribution. We call this vector field the “camera-sampling field.” We give the derivations and discuss some essential properties of the camera-sampling field, including flux, divergence, curl, gradient, level surface, and sampling patterns. This vector field reveals camera-sampling concisely and facilitates camera sampling analysis in many applications. The usage for this vector field in several computer graphics applications is introduced, such as determining the splat kernel for image-based rendering, texture filtering, mipmap level selection, level transition criteria for LOD and LDI-construction. These demonstrations verify the widely usage of camera-sampling field to many domains involving camera sampling.

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