  Unsupervised point cloud completion aims to infer the whole geometry of a
partial object observation without requiring partial-complete correspondence.
Differing from existing deterministic approaches, we advocate generative
modeling based unsupervised point cloud completion to explore the missing
correspondence. Specifically, we propose a novel framework that performs
completion by transforming a partial shape encoding into a complete one using a
latent transport module, and it is designed as a latent-space energy-based
model (EBM) in an encoder-decoder architecture, aiming to learn a probability
distribution conditioned on the partial shape encoding. To train the latent
code transport module and the encoder-decoder network jointly, we introduce a
residual sampling strategy, where the residual captures the domain gap between
partial and complete shape latent spaces. As a generative model-based
framework, our method can produce uncertainty maps consistent with human
perception, leading to explainable unsupervised point cloud completion. We
experimentally show that the proposed method produces high-fidelity completion
results, outperforming state-of-the-art models by a significant margin.
