Abstract
Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene. Due to its close relationship to semantic segmentation, many works approach these two tasks simultaneously and leverage the benefits of multi-task learning. However, most of them only considered simple strategies such as element-wise feature fusion, which may not lead to mutual promotion. In this work, we build a Bi-Directional Attention module on backbone neural networks for 3D point cloud perception, which uses similarity matrix measured from features for one task to help aggregate non-local information for the other task, avoiding the potential feature exclusion and task conflict. From comprehensive experiments on the three prevalent datasets, as well as ablation and efficiency studies, the superiority of our method is verified. Moreover, the mechanism of how bi-directional attention module helps joint instance and semantic segmentation is also analyzed.
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Acknowledgment
This work was funded by National Key Research & Development Plan of China (No. 2017YFB1002603), National Natural Science Foundation of China (61702301, 61772318) and Fundamental Research Funds of Shandong University.
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Wu, G., Pan, Z., Jiang, P., Tu, C. (2021). Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_13
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