  Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a
formidable challenge, primarily stemming from the sparse and unordered nature
of point cloud data. Especially for LiDAR point clouds, the domain discrepancy
becomes obvious across varying capture scenes, fluctuating weather conditions,
and the diverse array of LiDAR devices in use. While previous UDA methodologies
have often sought to mitigate this gap by aligning features between source and
target domains, this approach falls short when applied to 3D segmentation due
to the substantial domain variations. Inspired by the remarkable generalization
capabilities exhibited by the vision foundation model, SAM, in the realm of
image segmentation, our approach leverages the wealth of general knowledge
embedded within SAM to unify feature representations across diverse 3D domains
and further solves the 3D domain adaptation problem. Specifically, we harness
the corresponding images associated with point clouds to facilitate knowledge
transfer and propose an innovative hybrid feature augmentation methodology,
which significantly enhances the alignment between the 3D feature space and
SAM's feature space, operating at both the scene and instance levels. Our
method is evaluated on many widely-recognized datasets and achieves
state-of-the-art performance.
