In feature-learning based point cloud registration, the correct correspondence construction is vital for the subsequent transformation estimation. However, it is still a challenge to extract discriminative features from point cloud, especially when the input is partial and composed by indistinguishable surfaces (planes, smooth surfaces, etc.). As a result, the proportion of inlier correspondences that precisely match points between two unaligned point clouds is beyond satisfaction. Motivated by this, we devise several techniques to promote feature-learning based point cloud registration performance by leveraging inlier correspondences proportion: a pyramid hierarchy decoder to characterize point features in multiple scales, a consistent voting strategy to maintain consistent correspondences and a geometry guided encoding module to take geometric characteristics into consideration. Based on the above techniques, We build our Geometry-guided Consistent Network (GCNet), and challenge GCNet by indoor, outdoor and object-centric synthetic datasets. Comprehensive experiments demonstrate that GCNet outperforms the state-of-the-art methods and the techniques used in GCNet is model-agnostic, which could be easily migrated to other feature-based deep learning or traditional registration methods, and dramatically improve the performance. The code is available at https://github.com/zhulf0804/NgeNet.

翻译：在基于地貌学习的点云登记中,正确的通信构造对于随后的转换估计至关重要。然而,从点云中提取区分性特征仍然是一项挑战,特别是当输入是局部的,并且由无法区分的表面(飞机、光滑的表面等)组成时。结果,精确匹配两个不相接点云之间的点点点点的内线通信比例是无法令人满意的。为此,我们设计了一些技术,通过利用不相干通信的比例,促进基于地貌学习的点云登记性能:在多个尺度中确定点特征的金字塔级级分解码器、保持一致通信的一致投票战略以及考虑到几何特征的几何制导编码模块。基于上述技术,我们建立了我们的大地测量-制导一致网络(GCNet),并通过室内、室外和以物体为中心的合成数据集挑战GCNet。全面实验表明,GCNet超越了最新技术以及GCNet中所使用的技术是模型-agnotic,可以很容易迁移到其他基于地貌的深层次或传统注册方法。