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.

本研究提出使用超球形嵌入来优化点云补全问题中的编码器 - 解码器模型，通过对编码器中提取的特征进行转换和归一化，使得嵌入的分布更加紧凑，从而提高了训练的稳定性和泛化性能，并在单任务和多任务学习中实现了更好的补全效果。