  Most real-world 3D measurements from depth sensors are incomplete, and to
address this issue the point cloud completion task aims to predict the complete
shapes of objects from partial observations. Previous works often adapt an
encoder-decoder architecture, where the encoder is trained to extract
embeddings that are used as inputs to generate predictions from the decoder.
However, the learned embeddings have sparse distribution in the feature space,
which leads to worse generalization results during testing. To address these
problems, this paper proposes a hyperspherical module, which transforms and
normalizes embeddings from the encoder to be on a unit hypersphere. With the
proposed module, the magnitude and direction of the output hyperspherical
embedding are decoupled and only the directional information is optimized. We
theoretically analyze the hyperspherical embedding and show that it enables
more stable training with a wider range of learning rates and more compact
embedding distributions. Experiment results show consistent improvement of
point cloud completion in both single-task and multi-task learning, which
demonstrates the effectiveness of the proposed method.
