最近，基於3D重建學習方法越來越流行，與傳統多視圖重建算法不同，僅已單視圖重建3D圖像，更具有挑戰性。現行流行的表示方法大概可以分為3種: 基於點雲的表示、基於體素的表示以及網格的表示。在本文中，我們改進了一個新型的深度卷積神經網路架構Occupancy Networks[1]。將網路的Encoder替換成新的方法EfficientNet，接下來將Decoder更改成不同的架構DenseNet。使用ShapeNet[6]資料集裡面的飛機、車子和椅子3個類別作為實驗。我們將資料分割成訓練集、驗證集以及測試集。實驗表明了，我們提出的新型網路不管在視覺方面，還是定性的數據的比較上都有比原本的Occupancy Networks更加好的成績。

Recently, learning methods based on 3D reconstruction have become increasingly
popular. Unlike traditional multi-view reconstruction algorithms, single-view reconstruction of 3D images is more challenging. Presently popular representation methods can be roughly divided into three types: point cloud-based representation, voxel-based representation and grid representation. In this paper, we improve a new type of deep convolutional neural network architecture Occupancy Networks. We replace the Encoder of the network with the new method EfficientNet, and then change the Decoder to a different architecture DenseNet. We use the ShapeNet dataset in the three categories of planes, cars, and chairs as experiments. The data set is split into training set, validation set, and test set. Experiments show that our proposed network outperforms the Occupancy Networks in both visual and quantitative comparison.

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