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- W3112996878 abstract "Self-attention networks have revolutionized natural language processing andare making impressive strides in image analysis tasks such as imageclassification and object detection. Inspired by this success, we investigatethe application of self-attention networks to 3D point cloud processing. Wedesign self-attention layers for point clouds and use these to constructself-attention networks for tasks such as semantic scene segmentation, objectpart segmentation, and object classification. Our Point Transformer designimproves upon prior work across domains and tasks. For example, on thechallenging S3DIS dataset for large-scale semantic scene segmentation, thePoint Transformer attains an mIoU of 70.4% on Area 5, outperforming thestrongest prior model by 3.3 absolute percentage points and crossing the 70%mIoU threshold for the first time." @default.
- W3112996878 created "2020-12-21" @default.
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- W3112996878 creator A5052856441 @default.
- W3112996878 creator A5078109015 @default.
- W3112996878 date "2020-12-16" @default.
- W3112996878 modified "2023-09-23" @default.
- W3112996878 title "Point Transformer." @default.
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- W3112996878 hasPublicationYear "2020" @default.
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