  Self-supervised 3D representation learning aims to learn effective
representations from large-scale unlabeled point clouds. Most existing
approaches adopt point discrimination as the pretext task, which assigns
matched points in two distinct views as positive pairs and unmatched points as
negative pairs. However, this approach often results in semantically identical
points having dissimilar representations, leading to a high number of false
negatives and introducing a "semantic conflict" problem. To address this issue,
we propose GroupContrast, a novel approach that combines segment grouping and
semantic-aware contrastive learning. Segment grouping partitions points into
semantically meaningful regions, which enhances semantic coherence and provides
semantic guidance for the subsequent contrastive representation learning.
Semantic-aware contrastive learning augments the semantic information extracted
from segment grouping and helps to alleviate the issue of "semantic conflict".
We conducted extensive experiments on multiple 3D scene understanding tasks.
The results demonstrate that GroupContrast learns semantically meaningful
representations and achieves promising transfer learning performance.
