Semantic enhancement based adaptive geometric encoding network for low overlap point cloud registration-Reference-Cited by-同舟云学术

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Semantic enhancement based adaptive geometric encoding network for low overlap point cloud registration

Published:2024-01 Issue: Volume:81 Page:102615
ISSN:0141-9382
Container-title:Displays
language:en
Short-container-title:Displays

Author:

Zhao Yuehua,Zhang Jiguang,Xu Shibiao,Ma Jie,Wang Huishan

Funder

Beijing University of Posts and Telecommunications

Institute of Automation Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Human-Computer Interaction

Reference48 articles.

1. S.A. Ali, K. Kahraman, G. Reis, et al., RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021, pp. 13100–13110.

2. H. Deng, T. Birdal, S. Ilic, 3D Local Features for Direct Pairwise Registration, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3244–3253.

3. H. Deng, T. Birdal, S. Ilic, PPFNet: Global context aware local features for robust 3D point matching, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 195–205.

4. Learning general and distinctive 3D local deep descriptors for point cloud registration;Poiesi;IEEE Trans. Pattern Anal. Mach. Intell.,2022

5. C. Choy, W. Dong, V. Koltun, Deep Global Registration, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 2514–2523.

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1. RTHEN: Unsupervised deep homography estimation based on dynamic attention for repetitive texture image stitching;Displays;2024-04

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