  Recent deep networks that directly handle points in a point set, e.g.,
PointNet, have been state-of-the-art for supervised learning tasks on point
clouds such as classification and segmentation. In this work, a novel
end-to-end deep auto-encoder is proposed to address unsupervised learning
challenges on point clouds. On the encoder side, a graph-based enhancement is
enforced to promote local structures on top of PointNet. Then, a novel
folding-based decoder deforms a canonical 2D grid onto the underlying 3D object
surface of a point cloud, achieving low reconstruction errors even for objects
with delicate structures. The proposed decoder only uses about 7% parameters of
a decoder with fully-connected neural networks, yet leads to a more
discriminative representation that achieves higher linear SVM classification
accuracy than the benchmark. In addition, the proposed decoder structure is
shown, in theory, to be a generic architecture that is able to reconstruct an
arbitrary point cloud from a 2D grid. Our code is available at
http://www.merl.com/research/license#FoldingNet
