We present a new approach to 3D object representation where a neural network
encodes the geometry of an object directly into the weights and biases of a
second 'mapping' network. This mapping network can be used to reconstruct an
object by applying its encoded transformation to points randomly sampled from a
simple geometric space, such as the unit sphere. We study the effectiveness of
our method through various experiments on subsets of the ShapeNet dataset. We
find that the proposed approach can reconstruct encoded objects with accuracy
equal to or exceeding state-of-the-art methods with orders of magnitude fewer
parameters. Our smallest mapping network has only about 7000 parameters and
shows reconstruction quality on par with state-of-the-art object decoder
architectures with millions of parameters. Further experiments on feature
mixing through the composition of learned functions show that the encoding
captures a meaningful subspace of objects.

通过神经网络将几何结构编码到权重和偏置中，通过映射网络重构对象，可在 7000 参数的情况下达到与百万参数状态下的最新型物体解码器架构同等甚至更高的精度。