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 this https URL

本研究将形状共分割作为表示学习问题，并介绍了 BAE-NET（一种分支自编码器网络），用于解决此任务。BAE-NET 是一种无监督的网络，使用形状重建损失在未分割形状集合上进行训练，该网络的编码器使用卷积神经网络对输入的形状进行编码，而解码器将结果特征码与点坐标连接起来，并输出一个指示该点是否在形状内 / 外的值。重要的是，解码器是分支式的：每个分支都学习形状集合中常见部分的紧凑表示。通过将形状重建损失与标签损失结合使用，BAE-NET 可以轻松进行单次学习。我们展示了 BAE-NET 的无监督、弱监督和单次学习结果，证明仅使用少量范例，我们的网络通常可以胜过在数百个分割形状上训练的最先进监督方法。