This paper advocates the use of implicit surface representation in
autoencoder-based self-supervised 3D representation learning. The most popular
and accessible 3D representation, i.e., point clouds, involves discrete samples
of the underlying continuous 3D surface. This discretization process introduces
sampling variations on the 3D shape, making it challenging to develop
transferable knowledge of the true 3D geometry. In the standard autoencoding
paradigm, the encoder is compelled to encode not only the 3D geometry but also
information on the specific discrete sampling of the 3D shape into the latent
code. This is because the point cloud reconstructed by the decoder is
considered unacceptable unless there is a perfect mapping between the original
and the reconstructed point clouds. This paper introduces the Implicit
AutoEncoder (IAE), a simple yet effective method that addresses the sampling
variation issue by replacing the commonly-used point-cloud decoder with an
implicit decoder. The implicit decoder reconstructs a continuous representation
of the 3D shape, independent of the imperfections in the discrete samples.
Extensive experiments demonstrate that the proposed IAE achieves
state-of-the-art performance across various self-supervised learning
benchmarks.

本文主张在基于自监督的三维表征学习中使用隐式曲面表征，通过提出的隐式自编码器（IAE）方法，解决了点云重建过程中的离散化采样误差问题，并在各种自监督学习基准测试中达到了最先进的性能水平。