Adaptive Siamese Masked Autoencoder with Global Optimization for Unsupervised Point Cloud Shape Correspondence

Abstract

Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between point clouds without annotated data. Ensuring efficiency and accuracy is crucial for practically implementing point cloud shape correspondence. Although the current methods have achieved desirable performance, the nature of encoding at dense points limits their application in actual scenarios. Moreover, independently computing per-point correspondences results in numerous multiple-to-one erroneous correspondences. To address these issues, we present an Adaptive siamese Masked autoencoder with Global Optimization (AMIGO), comprising a siamese masked autoencoder and a global optimization module. In the siamese masked autoencoder, we downsample the input point cloud and employ adaptive siamese mask operations to boost the coding capabilities of the encoder, thereby mitigating the information loss caused by downsampling. In the global optimization module, optimal transport is only utilized to generate pseudo-labels during the training phase, facilitating the efficient global planning of the correspondence results. Extensive experiments on four standard human and animal benchmarks demonstrate that AMIGO surpasses existing methods with remarkable margins, achieving new state-of-the-art results.