Transcription of Point Transformer
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Point Transformer
Point TransformerHengshuang Zhao1,2Li Jiang3 Jiaya Jia3 Philip Torr1 Vladlen Koltun41 University of Oxford2 The University of Hong Kong3 The Chinese University of Hong Kong4 Intel LabsAbstractSelf-attention networks have revolutionized natural lan-guage processing and are making impressive strides in im-age analysis tasks such as image classification and objectdetection. Inspired by this success, we investigate the ap-plication of self-attention networks to 3D Point cloud pro-cessing. We design self-attention layers for Point clouds anduse these to construct self-attention networks for tasks suchas semantic scene segmentation, object part segmentation,and object classification. Our Point Transformer design im-proves upon prior work across domains and tasks. For ex-ample, on the challenging S3 DIS dataset for large-scale se-mantic scene segmentation, the Point Transformer attainsan mIoU of on Area 5, outperforming the strongestprior model by absolute percentage points and crossingthe 70% mIoU threshold for the first Introduction3D data arises in many application areas such as au-tonomous driving, augmented reality, and robotics.
Point Transformer Figure 1. The Point Transformer can serve as the backbone for var-ious 3D point cloud understanding tasks such as object classifica-tion, object part segmentation, and semantic scene segmentation. in natural language processing [39,45,5,4,51] and image analysis [10,28,54]. The transformer family of models is
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