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Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing.

, , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 46 (5): 3290-3304 (May 2024)

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Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing., , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 46 (5): 3290-3304 (May 2024)3D-Rotation-Equivariant Quaternion Neural Networks., , , , and . CoRR, (2019)3D-Rotation-Equivariant Quaternion Neural Networks., , , , and . ECCV (20), volume 12365 of Lecture Notes in Computer Science, page 531-547. Springer, (2020)FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving., , , , , , , , , and 4 other author(s). CoRR, (2023)Utility Analysis of Network Architectures for 3D Point Cloud Processing., , , , and . CoRR, (2019)An Automatic Image Segmentation Algorithm Based on Three-Way Decisions., , , , and . ICACI, page 118-125. IEEE, (2020)A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification., , , and . CoRR, (2022)A CLAIM Approach to Understanding the PointNet., , , and . ACAI, page 97-103. ACM, (2019)Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing., , , , , , and . CVPR, page 10703-10712. Computer Vision Foundation / IEEE, (2021)Explaining the PointNet: What Has Been Learned Inside the PointNet?, , , and . CVPR Workshops, page 71-74. Computer Vision Foundation / IEEE, (2019)