Abstract
An edge between two points describes rich information about the underlying surface. However, recent works merely use edge information as an ad hoc feature, which may undermine its effectiveness. In this study, we propose the Anisotropic Edge Modeling (AEM) block by which edges are modeled adaptively. As a result, the local feature representation is optimized where edges (e.g., object boundaries defined by ground truth) are appropriately enhanced. By stacking AEM blocks, AEM-Nets are constructed to tackle various point cloud understanding tasks. Extensive experiments demonstrate that AEM-Nets compare favorably to recent strong networks. In particular, AEM-Nets achieve state-of-the-art performance in object classification on ScanObjectNN, object segmentation on ShapeNet Part, and scene segmentation on S3DIS. Moreover, it is verified that AEM-Net outperforms the strong transformer-based method with significantly fewer parameters and FLOPs, achieving efficient learning. Qualitatively, the intuitive visualization of learned features successfully validates the effect of the AEM block.
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Acknowledgement
This work was partially supported by the projects commissioned by the New Energy and Industrial Technology Development Organization (JPNP18010 and JPNP20006), JSPS Grant-in-Aid for Scientific Research (21K12042), and Fundamental Research Funds for the Central Universities (DUT21RC(3)028).
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Xiu, H. et al. (2023). Optimizing Local Feature Representations of 3D Point Clouds with Anisotropic Edge Modeling. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_21
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