  We treat shape co-segmentation as a representation learning problem and
introduce BAE-NET, a branched autoencoder network, for the task. The
unsupervised BAE-NET is trained with a collection of un-segmented shapes, using
a shape reconstruction loss, without any ground-truth labels. Specifically, the
network takes an input shape and encodes it using a convolutional neural
network, whereas the decoder concatenates the resulting feature code with a
point coordinate and outputs a value indicating whether the point is
inside/outside the shape. Importantly, the decoder is branched: each branch
learns a compact representation for one commonly recurring part of the shape
collection, e.g., airplane wings. By complementing the shape reconstruction
loss with a label loss, BAE-NET is easily tuned for one-shot learning. We show
unsupervised, weakly supervised, and one-shot learning results by BAE-NET,
demonstrating that using only a couple of exemplars, our network can generally
outperform state-of-the-art supervised methods trained on hundreds of segmented
shapes. Code is available at https://github.com/czq142857/BAE-NET.
