  We propose a method for the unsupervised reconstruction of a
temporally-coherent sequence of surfaces from a sequence of time-evolving point
clouds, yielding dense, semantically meaningful correspondences between all
keyframes. We represent the reconstructed surface as an atlas, using a neural
network. Using canonical correspondences defined via the atlas, we encourage
the reconstruction to be as isometric as possible across frames, leading to
semantically-meaningful reconstruction. Through experiments and comparisons, we
empirically show that our method achieves results that exceed that state of the
art in the accuracy of unsupervised correspondences and accuracy of surface
reconstruction.
