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We present a deep convolutional decoder architecture that can generatevolumetric 3D outputs in a compute- and memory-efficient manner by using anoctree representation. The network learns to predict both the structure of theoctree, and the occupancy values of individual cells. This makes it aparticularly valuable technique for generating 3D shapes. In contrast tostandard decoders acting on regular voxel grids, the architecture does not havecubic complexity. This allows representing much higher resolution outputs witha limited memory budget. We demonstrate this in several application domains,including 3D convolutional autoencoders, generation of objects and whole scenesfrom high-level representations, and shape from a single image.

Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs