Point Clouds Registration is a fundamental and challenging problem in 3D computer vision. It has been shown that the isometric transformation is an essential property in rigid point cloud registration, but the existing methods only utilize it in the outlier rejection stage. In this paper, we emphasize that the isometric transformation is also important in the feature learning stage for improving registration quality. We propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (denoted as GMONet for short), which utilizes the graph matching method to explicitly exert the isometry preserving constraints in the point feature learning stage to improve %refine the point representation. Specifically, we %use exploit the partial graph matching constraint to enhance the overlap region detection abilities of super points ($i.e.,$ down-sampled key points) and full graph matching to refine the registration accuracy at the fine-level overlap region. Meanwhile, we leverage the mini-batch sampling to improve the efficiency of the full graph matching optimization. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method achieves competitive performance compared with the existing state-of-the-art baselines.
翻译:云云注册是3D计算机愿景中一个根本性和具有挑战性的问题。 已经显示, 等量转换是硬点云注册中的一个基本属性, 但现有方法仅在顶点拒绝阶段使用。 在本文中, 我们强调, 等量转换对于提高注册质量的特征学习阶段也很重要。 我们提议在基于 3D 的计算机愿景中采用一个基于 3D 的 底线 的 匹配 { Net} 工作( 以 GMONET 表示短调) 的 基本问题 。 它使用 图形匹配方法, 明确应用 点特征学习阶段的偏差保存限制来改进点代表。 具体地说, 我们使用部分图形匹配限制来提高超点( e. $ 下标出的关键点 ) 的重叠区域检测能力, 并用 完整的图表匹配 精细级重叠区域 的 。 与此同时, 我们利用 微量匹配抽样取样方法来提高完整图表匹配优化的效率 。 在高分析性点学习阶段, 我们利用了部分的图像匹配模型评估方法 。