BAE-NET: Branched Autoencoder for Shape Co-Segmentation

Understand

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.