GP-Aligner: Unsupervised Groupwise Nonrigid Point Set Registration Based on Optimizable Group Latent Descriptor

Abstract

In this paper, we propose a novel unsupervised method named GP-Aligner to address the problem of groupwise non-rigid point set registration. Compared to previous non-learning-based approaches, the proposed method gains competitive advantages by leveraging deep neural networks to effectively and efficiently 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 per-shape features and their correlations, our model leverages a model-free learnable latent descriptor to characterize shape correlations among groups. More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the 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 network is further constructed to predict the coherent flow fields to optimally deform the input groups of shapes to the aligned ones. 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 mean 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 the state-of-the-art methods for groupwise point set registration. Moreover, GP-Aligner exhibits high efficiency in aligning a large number of groups of real-world 3D shapes.