本論文對 FVR 做了一個完整的研究，並分析了 FVR 因再取樣的問題而導致重疊影 像的發生，也實作了許多濾波器來進行效能與品質上的比較。我們發現將三線性濾波器 與三次曲線濾波器的合併使用可以有效的消除重疊影像，但是卻帶來龐大的計算負擔。
我們也發現將原始的立體資料在空間上延伸兩倍，然後配合計算負擔較少的三線性濾波 器會有更好的效果，只是在記憶體空間上會多出一倍。
我們也設計了兩種進行分類影像的方式，一是預先影像處理，另一是貝茲曲線轉換 函數。這兩者皆是利用傅立葉轉換具有線性組合的特性來設計，雖然 Levoy [9] [10]已經 發現這樣的特性並且設計了 FVR 的著色模型，但卻尚未提出比較有效的線性組合方程 式來進行分類顯像。Nagy [13]雖然提出一個將非線性的步進函數以線性組合方式來進行 分類顯像，但分類的限制過大。比較之下，我們所提出的貝茲曲線轉換函數較為有效且 容易實作，使用者也可以很容易地產生出想要進行分類的轉換函數，並且能夠即時地描 繪出投影結果。
FVR 尚有許多問題需要克服，在時間上我們希望能找出更經濟且不影響品質的濾波 器，因為根據我們的實驗結果，取樣濾波的動作在整體運算時間中所佔的比重是最大，
而不是 2 維的反傅立葉轉換。空間上也是急需克服的問題，我們所提出的轉換函數會使 用不少的記憶體空間，Hartley transform [27]可以稍微解決這個問題，它可讓我們減少一 半的記憶體空間，以及 Westenberg [28]提出利用小波轉換(Wavelet transform)來達成 Level-of-detail。因此，我們會利用這兩項技術來改善記憶體空間的問題。
另外，我們除了期盼 GPU 的發展，能夠有更強大的功能來支援 FVR，包括效能、
記憶體空間、記憶體的存取速度…等，也希望能自行設計支援 FVR 演算法的硬體，針 對上述的問題加以克服。
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