  Self-attention networks have revolutionized natural language processing and
are making impressive strides in image analysis tasks such as image
classification and object detection. Inspired by this success, we investigate
the application of self-attention networks to 3D point cloud processing. We
design self-attention layers for point clouds and use these to construct
self-attention networks for tasks such as semantic scene segmentation, object
part segmentation, and object classification. Our Point Transformer design
improves upon prior work across domains and tasks. For example, on the
challenging S3DIS dataset for large-scale semantic scene segmentation, the
Point Transformer attains an mIoU of 70.4% on Area 5, outperforming the
strongest prior model by 3.3 absolute percentage points and crossing the 70%
mIoU threshold for the first time.
