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PointMixer: MLP-Mixer for Point Cloud Understanding

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13687))

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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.

J. Choe and C. Park—Both authors have equally contributed to this work.

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Notes

  1. 1.

    We set the relation of terminologies as \(\text {layer}\subset \text {block}\subset \text {network}\).

  2. 2.

    To deal with unordered points, layers are permutation-invariant (\(f_\text {layer}:\mathcal {X}_i\rightarrow \textbf{y}_i\)) and blocks are permutation-equivariant (\(f_\text {block}:\mathcal {X}\rightarrow \mathcal {Y}\)).

  3. 3.

    Please refer to the supplementary material for further details.

  4. 4.

    There is a chance to collect variable number of points after an inverse mapping \(\mathcal {M}_{i}^{-1}\).

  5. 5.

    Since there is no official release of codes, we use the best implementation of Point Transformer available in the public domain, which contains the official code provided by the authors of Point Transformer and reproduces the reported accuracy.

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Acknowledgements

(1) IITP grant funded by the Korea government (MSIT) (No. 2019-0-01906, Artificial Intelligence Graduate School Program (POSTECH)) and (2) Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea

    Jaesung Choe, Francois Rameau & In So Kweon

  2. Pohang University of Science and Technology (POSTECH), Pohang, South Korea

    Chunghyun Park & Jaesik Park

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  1. Jaesung Choe
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  5. In So Kweon
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Correspondence to Jaesung Choe .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Choe, J., Park, C., Rameau, F., Park, J., Kweon, I.S. (2022). PointMixer: MLP-Mixer for Point Cloud Understanding. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13687. Springer, Cham. https://doi.org/10.1007/978-3-031-19812-0_36

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