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R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision
- Source :
- Remote Sensing, Vol 15, Iss 7, p 1889 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 7
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.387fa6ee34615ae6086a6d6913ea6
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs15071889