  The past few years have witnessed the great success and prevalence of
self-supervised representation learning within the language and 2D vision
communities. However, such advancements have not been fully migrated to the
field of 3D point cloud learning. Different from existing pre-training
paradigms designed for deep point cloud feature extractors that fall into the
scope of generative modeling or contrastive learning, this paper proposes a
translative pre-training framework, namely PointVST, driven by a novel
self-supervised pretext task of cross-modal translation from 3D point clouds to
their corresponding diverse forms of 2D rendered images. More specifically, we
begin with deducing view-conditioned point-wise embeddings through the
insertion of the viewpoint indicator, and then adaptively aggregate a
view-specific global codeword, which can be further fed into subsequent 2D
convolutional translation heads for image generation. Extensive experimental
evaluations on various downstream task scenarios demonstrate that our PointVST
shows consistent and prominent performance superiority over current
state-of-the-art approaches as well as satisfactory domain transfer capability.
Our code will be publicly available at https://github.com/keeganhk/PointVST.
