自從深度學習開始火紅之後，使用深度學習作三維重建相關研究也越來越多了，
其中用來估計深度的方法也有很多種，我覺得當中非監督式的方法很有研究的價 值，有別於以往深度學習的方法使用端到端去進行監督，非監督式的方法可以說 是非常的新奇，相信有做過深度學習相關研究的人都知道標記數據的取得無非是 深度學習的難點之一，然而非監督式的方法資料取得非常的容易，我覺得這就是 非監督式的方法很大的優勢之一。
在本論文裡我們從各種不同的角度去思考如何讓非監督式的方法結果變得更 好，我們更改了網路架構、排除了一些造成錯誤估計的部分，也融入雙眼資料來 訓練我們的模型，從最後的實驗結果可以看出，我們的每個方法都是有效的，而 且我們的結果也比其它方法要來的好。
在最後我們也思考我們的研究未來還有哪些可以改善的地方，首先是我們證明 了靜態場景的有效性，因此如果我們有辦法可以直接找出場景中移動的物體進行 排除的話，例如使用光流之類的方法，那我們的結果還能夠更好，在來是更好的 網路架構，在深度學習中網路架構非常的重要，在我們的方法中證明了我們的網 路架構確得更改實是有效的，那麼如果能找到一個更適合這個問題的網路架構，
那麼結果也能夠更好，最後就是將我們的方法用在不同的資料集中，驗證我們的 方法能夠靈活的運用在各種不同的場景中。
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