Abstract
Point cloud analysis is a technique that performs analysis and processing of point cloud data. In the medical field, point cloud analysis has been widely used. However, the existing common neighbor aggregation module in point cloud analysis networks can only aggregate some of the neighbor features, which will lead to the omission of valid information and affect the performance of point cloud analysis, which may lead to serious consequences in the medical diagnosis process. In this paper, we improve the ability of point cloud analysis networks to extract complex biological structures by improving the neighbor aggregation module in point cloud analysis. Specifically, we enable the module to efficiently extract more adequate information by softening the max pooling function commonly used in the neighbor aggregation module. In particular, we improve 2.18% IoU on the IntrA dataset compared to the previous state-of-the-art method, and we also surpass the previous state-of-the-art method on the S3DIS dataset. Code is available at https://github.com/wfan1203/PointSWT.
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Acknowledgements
This work was supported by National Key R &D Program of China (No. 2022ZD0118202), the National Science Fund for Distinguished Young Scholars (No. 62025603), the National Natural Science Foundation of China (No. U21B2037, No. U22B2051, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, No. 62002305 and No. 62272401), and the Natural Science Foundation of Fujian Province of China (No. 2021J01002, No. 2022J06001).
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Wu, F., Qian, Y., Zheng, H., Zhang, Y., Zheng, X. (2024). A Novel Neighbor Aggregation Function for Medical Point Cloud Analysis. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_24
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