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

Improved Modeling of 3D Shapes with Multi-view Depth Maps