Reviews: Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

Originality: This paper is a novel combination of an existing method [7,21] for 2D images, to an existing task (point cloud feature learning). Given the success of [21], one would expect it also works for 3D representation where the spatial layout is equally or more important, which is confirmed by the results in this paper. The citations in this paper sufficiently cover related work. Quality: Most of the experimental results appear to be meaningful and support claimed advantages of this method: architecture-agnostic, avoids reconstruction metric, helps supervised down-stream tasks. But the comparison to alternative methods in Table 1 is weakened by the fact that model architectures used by the baseline methods are not mentioned.