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. The
source code is available at this https URL

提出了一种通过语言辅助的方法来学习点云特征，使用 LMMs 文本丰富语义概念，通过基于统计的显著特征选择实现去冗余和降低特征维度，进一步分析了文本对点云的对比训练的影响，实验证实所提出的方法在三维语义分割、三维物体检测和三维场景分类任务中学习到了有意义的点云特征，并获得了最先进或可比较的性能。