  In this paper, we propose a novel method named GP-Aligner to deal with the
problem of non-rigid groupwise point set registration. Compared to previous
non-learning approaches, our proposed method gains competitive advantages by
leveraging the power of deep neural networks to effectively and efficiently
learn to align a large number of highly deformed 3D shapes with superior
performance. Unlike most learning-based methods that use an explicit feature
encoding network to extract the per-shape features and their correlations, our
model leverages a model-free learnable latent descriptor to characterize the
group relationship. More specifically, for a given group we first define an
optimizable Group Latent Descriptor (GLD) to characterize the gruopwise
relationship among a group of point sets. Each GLD is randomly initialized from
a Gaussian distribution and then concatenated with the coordinates of each
point of the associated point sets in the group. A neural network-based decoder
is further constructed to predict the coherent drifts as the desired
transformation from input groups of shapes to aligned groups of shapes. During
the optimization process, GP-Aligner jointly updates all GLDs and weight
parameters of the decoder network towards the minimization of an unsupervised
groupwise alignment loss. After optimization, for each group our model
coherently drives each point set towards a middle, common position (shape)
without specifying one as the target. GP-Aligner does not require large-scale
training data for network training and it can directly align groups of point
sets in a one-stage optimization process. GP-Aligner shows both accuracy and
computational efficiency improvement in comparison with state-of-the-art
methods for groupwise point set registration. Moreover, GP-Aligner is shown
great efficiency in aligning a large number of groups of real-world 3D shapes.
