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- W3207466644 abstract "In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work. Code is publicly available at: https://github.com/engelnico/point-transformer" @default.
- W3207466644 created "2021-10-25" @default.
- W3207466644 creator A5027065196 @default.
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- W3207466644 date "2021-10-14" @default.
- W3207466644 modified "2023-09-27" @default.
- W3207466644 title "Point Transformer." @default.
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