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PointMixer: MLP-Mixer for Point Cloud Understanding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choe, Jaesung | - |
dc.contributor.author | Park, Chunghyun | - |
dc.contributor.author | Rameau, Francois | - |
dc.contributor.author | Park, Jaesik | - |
dc.contributor.author | Kweon, In So | - |
dc.date.accessioned | 2024-05-09T04:12:23Z | - |
dc.date.available | 2024-05-09T04:12:23Z | - |
dc.date.created | 2024-05-08 | - |
dc.date.created | 2024-05-08 | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Lecture Notes in Computer Science, Vol.13687, pp.620-640 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/10371/201290 | - |
dc.description.abstract | MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and Transformer. Despite its simplicity compared to Transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in image recognition tasks. Unlike images, point clouds are inherently sparse, unordered and irregular, which limits the direct use of MLP-Mixer for point cloud understanding. To overcome these limitations, we propose PointMixer, a universal point set operator that facilitates information sharing among unstructured 3D point cloud. By simply replacing token-mixing MLPs with Softmax function, PointMixer can mix features within/between point sets. By doing so, PointMixer can be broadly used for intra-set, inter-set, and hierarchical-set mixing. We demonstrate that various channel-wise feature aggregation in numerous point sets is better than self-attention layers or dense token-wise interaction in a view of parameter efficiency and accuracy. Extensive experiments show the competitive or superior performance of PointMixer in semantic segmentation, classification, and reconstruction against Transformer-based methods. | - |
dc.language | 영어 | - |
dc.publisher | Springer Verlag | - |
dc.title | PointMixer: MLP-Mixer for Point Cloud Understanding | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-031-19812-0_36 | - |
dc.citation.journaltitle | Lecture Notes in Computer Science | - |
dc.identifier.wosid | 000903590200036 | - |
dc.identifier.scopusid | 2-s2.0-85142704413 | - |
dc.citation.endpage | 640 | - |
dc.citation.startpage | 620 | - |
dc.citation.volume | 13687 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Park, Jaesik | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
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