We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
this https URL

本文介绍了一种新颖的端到端可学习的多视角三维点云拼接算法，该算法集成了两阶段流程（成对对准和全局优化），并且是当前领先技术的改进。