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PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

9 Aug 2023ICCV 2023 1arXiv:2308.04782archive 2025-07-28

Mingzhi Yuan, Kexue Fu, Zhihao LI, Yucong Meng, Manning Wang

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the advance of inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to achieve unsupervised registration. However, most of existing unsupervised methods follow a cascaded design or fuse RGB-D data in a unidirectional manner, which do not fully exploit the complementary information in the RGB-D data. To leverage the complementary information more effectively, we propose a network implementing multi-scale bidirectional fusion between RGB images and point clouds generated from depth images. By bidirectionally fusing visual and geometric features in multi-scales, more distinctive deep features for correspondence estimation can be obtained, making our registration more accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our method achieves new state-of-the-art performance. Code will be released at https://github.com/phdymz/PointMBF

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calculate_ratio_test phdymz/pointmbf/models/correspondence.py official repository ran no licence file found · pointer only · 9e0b951555ec102f · report
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normalize_quaternion phdymz/pointmbf/models/alignment.py official repository ran no licence file found · pointer only · b8ec9326edb1790e · report
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invert_quaternion phdymz/pointmbf/models/alignment.py official repository unverified no licence file found · pointer only · e9857f544a5c3471 · report

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Point Cloud Registration

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