  The scale and quality of point cloud datasets constrain the advancement of
point cloud learning. Recently, with the development of multi-modal learning,
the incorporation of domain-agnostic prior knowledge from other modalities,
such as images and text, to assist in point cloud feature learning has been
considered a promising avenue. Existing methods have demonstrated the
effectiveness of multi-modal contrastive training and feature distillation on
point clouds. However, challenges remain, including the requirement for paired
triplet data, redundancy and ambiguity in supervised features, and the
disruption of the original priors. In this paper, we propose a
language-assisted approach to point cloud feature learning (LAST-PCL),
enriching semantic concepts through LLMs-based text enrichment. We achieve
de-redundancy and feature dimensionality reduction without compromising textual
priors by statistical-based and training-free significant feature selection.
Furthermore, we also delve into an in-depth analysis of the impact of text
contrastive training on the point cloud. Extensive experiments validate that
the proposed method learns semantically meaningful point cloud features and
achieves state-of-the-art or comparable performance in 3D semantic
segmentation, 3D object detection, and 3D scene classification tasks.
