“…Other unsupervised and self-supervised tasks, such as point cloud representation, also benefited from recent deep learning advances. The representation learning aims to learn a data representation that facilitates other downstream tasks (Bengio et al, 2013), such as point cloud classification (Achlioptas et al, 2018;Hassani & Haley, 2019;Jiang et al, 2021;Rao et al, 2020;Remelli et al, 2019), segmentation (Hassani & Haley, 2019), semantic segmentation (Bachmann et al, 2021;Jiang et al, 2021), clustering (Remelli et al, 2019;Zamorski et al, 2020), up-sampling (Remelli et al, 2019) and reconstruction (Achlioptas et al, 2018;Bachmann et al, 2021;Zamorski et al, 2020). To address the point cloud representation learning task, these works usually combine an encoder with a decoder that aims to reconstruct the input, and, as a consequence, the encoder learns meaningful features to compactly represent the input point cloud.…”