We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image of the object, we can output a dense multi-view depth map representation of 3D objects. Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps. Our experimental results demonstrate the two-fold advantage of our approach. First, we can directly borrow architectures that work well in the 2D image domain to 3D. Second, we can effectively generate high-resolution 3D shapes with low computational memory. Our quantitative evaluations show that our method is superior to existing depth map methods for reconstructing and synthesizing 3D objects and is competitive with other representations, such as point clouds, voxel grids, and implicit functions.

中文翻译：

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使用多视图深度图改进 3D 形状的建模

我们通过利用使用 CNN 生成 2D 图像的最新进展，提出了一个简单而有效的通用框架，用于建模 3D 形状。仅使用对象的单个深度图像，我们就可以输出 3D 对象的密集多视图深度图表示。我们的简单编码器-解码器框架由新颖的身份编码器和类条件视点生成器组成，可生成 3D 一致的深度图。我们的实验结果证明了我们方法的双重优势。首先，我们可以直接将在 2D 图像域中运行良好的架构借用到 3D。其次，我们可以有效地以低计算内存生成高分辨率 3D 形状。