论文标题
LRF-NET：学习3D本地形状描述和匹配的本地参考帧
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
论文作者
论文摘要
本地参考框架（LRF）在3D局部形状描述和匹配中起关键作用。但是，大多数现有的LRF是手工制作的，并且具有有限的重复性和鲁棒性。本文提出了通过仅需要弱监督的暹罗网络学习LRF的首次尝试。特别是，我们认为本地表面中的每个相邻点为LRF构造做出了独特的贡献，并通过学习的权重衡量此类贡献。在解决不同应用程序方案的三个公共数据集上进行了广泛的分析和比较实验，表明LRF-NET比几种最新的LRF方法更可重复，更健壮（LRF-NET仅在一个数据集中训练）。此外，当匹配3D点云时，LRF-NET可以显着提高局部形状描述和6-DOF姿势估计性能。
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of 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). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.
