The main goal of point cloud registration in Multi-View Partial (MVP) Challenge 2021 is to estimate a rigid transformation to align a point cloud pair. The pairs in this competition have the characteristics of low overlap, non-uniform density, unrestricted rotations and ambiguity, which pose a huge challenge to the registration task. In this report, we introduce our solution to the registration task, which fuses two deep learning models: ROPNet and PREDATOR, with customized ensemble strategies. Finally, we achieved the second place in the registration track with 2.96546, 0.02632 and 0.07808 under the the metrics of Rot\_Error, Trans\_Error and MSE, respectively.
翻译:2021年多视部分挑战(MVP)中点云登记的主要目标是估算硬质变换,以对点云对齐。本次竞争中的对口具有低重叠、非统一密度、无限制旋转和模糊性的特点,对登记任务构成巨大挑战。在本报告中,我们介绍了对登记任务的解决办法,这结合了两个深层学习模式:ROPNet和PREDATOR,它们有定制的组合战略。最后,我们在登记轨道上分别取得了2.96546、0.02632和0.07808的第二位,分别以Rot ⁇ Error、Trans ⁇ Error和MSE为标准。