期刊
SENSORS
卷 20, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s20185086
关键词
point cloud; local reference frame; deep learning
资金
- National Natural Science Foundation of China [U1913602]
- National Key R&D Program of China [2018YFB1305504]
- Natural Science Basic Research Plan in Shaanxi Province of China [2020JQ-210]
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt 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 gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). We show that LRFNet achieves 0.686 MeanCos performance on the UWA 3D modeling (UWA3M) dataset, outperforming the closest method by 0.18. In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.
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