UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inherently exists uncertainty between the overlapping and non-overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty-aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation-invariant geometry-aware feature representations.With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state-of-the-art methods.
Description
CCS Concepts: Computing methodologies → Point-based models
@article{10.1111:cgf.14659,
journal = {Computer Graphics Forum},
title = {{UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration}},
author = {Chen, Zhilei and Chen, Honghua and Gong, Lina and Yan, Xuefeng and Wang, Jun and Guo, Yanwen and Qin, Jing and Wei, Mingqiang},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14659}
}