  We introduce a deep recursive octree network for the compression of 3D voxel
data. Our network compresses a voxel grid of any size down to a very small
latent space in an autoencoder-like network. We show results for compressing
32, 64 and 128 grids down to just 80 floats in the latent space. We demonstrate
the effectiveness and efficiency of our proposed method on several publicly
available datasets with three experiments: 3D shape classification, 3D shape
reconstruction, and shape generation. Experimental results show that our
algorithm maintains accuracy while consuming less memory with shorter training
times compared to existing methods, especially in 3D reconstruction tasks.
