積極式可視度取樣法

Abstract

我們提出一個針對一般三維場景的以區域為基本之積極式可視度取樣演算法。我們的演算法利用影像空間中樣本的深度與顏色資訊，建立一個重要度函數來表示一個view cell表面上的潛在可視度集合(PVS)之可靠度，並根據該函數將取樣點放在最佳的位置。 此重要度函數能導引可視度取樣點到場景中的深度不連續處，以取得更多可視物體並降低視覺誤差。顏色資訊可以幫助判斷該視覺誤差是否明顯。相比幾個前人提出的方法，我們的實驗顯示出我們的取樣方法能有效地增加PVS的精確度與計算速度。

We present an aggressive region-based visibility sampling algorithm for general 3D scenes. Our algorithm exploits the depth and color information of samples in the image space to construct an importance function that represents the reliability of the potentially visible set (PVS) of a view cell boundary, and places samples at the optimal positions according to the importance function. The importance function indicates and guides visibility samples to depth discontinuities of the scene such that more visible objects can be sampled to reduce the visual errors. The color information can help judge whether the visual errors are significant or not. Our experiments show that our sampling approach can effectively improve the PVS accuracy and computational speed compared to the adaptive approach proposed in [NB04] and the object-based approach in [WWZ+06].