  3D point cloud segmentation has made tremendous progress in recent years.
Most current methods focus on aggregating local features, but fail to directly
model long-range dependencies. In this paper, we propose Stratified Transformer
that is able to capture long-range contexts and demonstrates strong
generalization ability and high performance. Specifically, we first put forward
a novel key sampling strategy. For each query point, we sample nearby points
densely and distant points sparsely as its keys in a stratified way, which
enables the model to enlarge the effective receptive field and enjoy long-range
contexts at a low computational cost. Also, to combat the challenges posed by
irregular point arrangements, we propose first-layer point embedding to
aggregate local information, which facilitates convergence and boosts
performance. Besides, we adopt contextual relative position encoding to
adaptively capture position information. Finally, a memory-efficient
implementation is introduced to overcome the issue of varying point numbers in
each window. Extensive experiments demonstrate the effectiveness and
superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code
is available at https://github.com/dvlab-research/Stratified-Transformer.
