Reviews: PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

Originality: - L3: "to the best of our knowledge, there is no method yet to achieve domain adaptation on 3D data, especially point cloud data" see below [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud, Wu et al 2018] proposes a domain adaptation pipeline for 3D lidar point cloud to reduce distribution gap between synthetic and real data. Technically the above two are operating in image space (depth semantic segmentation maps, and BEV of point cloud, respectively) but the underlying goal is still to model 3D information from point cloud. The first paper is from 2018 so I do think this paper over claims this'first to do domain adaptation in 3D data' statement a bit. Although it's worth noting that this paper explores point based representation rather than image based, and for classification task rather than point segmentation. But I think the similarity and different should be mentioned and discussed.