Better match 3D point clouds having partial overlap using task-specific features with EDFNet

Better match 3D point clouds having partial overlap using task-specific features with EDFNet

Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds

arXiv paper abstract https://arxiv.org/abs/2210.14899v1

arXiv PDF paper https://arxiv.org/pdf/2210.14899v1.pdf

Existing learning-based point feature descriptors are usually task-agnostic, which pursue describing the individual 3D point clouds as accurate as possible.

However, the matching task aims at describing the corresponding points consistently across different 3D point clouds.

... propose to learn a robust task-specific feature descriptor to consistently describe the correct point correspondence under interference.

... First, ... augment the matchability of correspondences by utilizing their repetitive local structure

... Second, ... propose a dynamical fusion module to jointly use different scale features ... analyze the consistency of them to judge the clean ones and perform larger aggregation weights on them during fusion

... Extensive evaluations validate that EDFNet learns a task-specific descriptor, which achieves state-of-the-art or comparable performance for robust matching of 3D point clouds.

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