Deep Models with Fusion Strategies for MVP Point Cloud Registration

Changwei Lin; Dongrui Liu; Francisco G\'omez-Fern\'andez; Lifa Zhu; Xin Li

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Deep Models with Fusion Strategies for MVP Point Cloud Registration

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arxiv 2110.09129 v1 pith:SN4ISLDF submitted 2021-10-18 cs.CV

Deep Models with Fusion Strategies for MVP Point Cloud Registration

Lifa Zhu , Changwei Lin , Dongrui Liu , Xin Li , Francisco G\'omez-Fern\'andez This is my paper

classification cs.CV

keywords registrationcloudpointchallengedeeperrormodelsstrategies

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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.

Deep Models with Fusion Strategies for MVP Point Cloud Registration

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