近年來隨著3D列印與網際網路快速發展，豐富的三維模型資源，使得迫切需要一個三維模型檢索系統。本論文提出一個基於視圖(view-based)特徵之三維模型檢索系統，採用多張二維影像表示一個三維模型。因為二維影像具有高辨別率，基於視圖特徵演算法在近幾年中成為三維模型檢索領域的熱門議題。不同於以往的三維模型檢索系統，本論文最初只需要對資料庫模型做平移與縮放正規化，因為所提方法具有旋轉不變性，旋轉正規化可以被省略。首先在均勻球體座標視點抓取三維模型多張二維影像，並且利用具有旋轉、縮放、平移不變性的傅立葉描述子(Fourier Descriptor)與 Zernike 矩(Zernike Moments)表示二維影像特徵，再以深度學習中，非監督式自編碼器(Autoencoder)提取每個模型精練過的特徵向量，以提升檢索效能。模型相似度計算採用改良式郝斯多夫距離(Modified Hausdorff Distance)，以克服模型旋轉變量。最後使用普林斯頓形狀評判標準(Princeton Shape Benchmark，PSB)資料庫做檢索效能評判，證明本論文較其他方法有較好的檢索效能。

As 3D printer and Internet are rapid expansion. The increasing number of 3D models on the Internet makes the demand on 3D model retrieval systems. In this thesis, a 3D models retrieval system based on view-based feature is proposed. The view-based 3D model retrieval system represents a 3D model by its projected views. It became a popular branch in 3D model analysis in recent years owing to the high discriminative property of 2D views. Different from previous algorithms that perform the normalization for rotation invariance, we only normalize the translation and the scale of the 3D models in the dataset. Because our method is rotation-invariant, the normalization for rotation is unnecessary and can be ignore. First of all, we create 2D projected views rendered from a sphere with the number of viewpoints uniformly distributed on it. And extract Fourier descriptor and Zernike Moments descriptor to get translation, rotation and scaling invariant feature. After that we utilize unsupervised learning method, Autoencoder, to refine a compact feature vector of each models and improve the retrieval performance. The (dis)similarity between two 3D models is measured by a Modified Hausdorff Distance function to conquer model rotation invariant. Exhaustive experimental results on Princeton Shape Benchmark Dataset confirm that our method achieves better retrieval performance than other methods.

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