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The local reference frame (LRF) acts as a critical role in 3D local shapedescription and matching. However, most of existing LRFs are hand-crafted andsuffer from limited repeatability and robustness. This paper presents the firstattempt to learn an LRF via a Siamese network that needs weak supervision only.In particular, we argue that each neighboring point in the local surface givesa unique contribution to LRF construction and measure such contributions vialearned weights. Extensive analysis and comparative experiments on three publicdatasets addressing different application scenarios have demonstrated thatLRF-Net is more repeatable and robust than several state-of-the-art LRF methods(LRF-Net is only trained on one dataset). In addition, LRF-Net cansignificantly boost the local shape description and 6-DoF pose estimationperformance when matching 3D point clouds.

LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching