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.
@article{arxiv.2110.09129,
title = {Deep Models with Fusion Strategies for MVP Point Cloud Registration},
author = {Lifa Zhu and Changwei Lin and Dongrui Liu and Xin Li and Francisco Gómez-Fernández},
journal= {arXiv preprint arXiv:2110.09129},
year = {2021}
}
Comments
Point cloud registration competition, ICCV21 workshop. Substantial text overlap with arXiv:2107.02583