Neighborhood-aware Geometric Encoding Network for Point Cloud Registration

• URL: http://arxiv.org/abs/2201.12094v1
• Date: Fri, 28 Jan 2022 13:04:54 GMT
• Title: Neighborhood-aware Geometric Encoding Network for Point Cloud Registration
• Authors: Lifa Zhu, Haining Guan, Changwei Lin, Renmin Han
• Abstract summary: Neighborhood-aware Geometric. Network (NgeNet) for accurate point cloud registration. NgeNet is model-agnostic, which could be easily migrated to other networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The distinguishing geometric features determine the success of point cloud registration. However, most point clouds are partially overlapping, corrupted by noise, and comprised of indistinguishable surfaces, which makes it a challenge to extract discriminative features. Here, we propose the Neighborhood-aware Geometric Encoding Network (NgeNet) for accurate point cloud registration. NgeNet utilizes a geometric guided encoding module to take geometric characteristics into consideration, a multi-scale architecture to focus on the semantically rich regions in different scales, and a consistent voting strategy to select features with proper neighborhood size and reject the specious features. The awareness of adaptive neighborhood points is obtained through the multi-scale architecture accompanied by voting. Specifically, the proposed techniques in NgeNet are model-agnostic, which could be easily migrated to other networks. Comprehensive experiments on indoor, outdoor and object-centric synthetic datasets demonstrate that NgeNet surpasses all of the published state-of-the-art methods. The code will be available at https://github.com/zhulf0804/NgeNet.

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