Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging
Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging
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DOI：
10.1109/tci.2021.3111572
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发表时间：
2021-01-01
影响因子：
5.4
通讯作者：
McLaughlin, Stephen
中科院分区：
文献类型：
--
作者：
Halimi, Abderrahim;Maccarone, Aurora;McLaughlin, Stephen
当使用多个波长时，或者当在高噪声环境（例如，通过遮光剂成像）。本文提出了一种分层贝叶斯算法的多光谱单光子激光雷达数据在这样的环境中的鲁棒重建。该算法利用多尺度信息来提供稳健的深度和反射率估计及其不确定性，以帮助决策。所提出的基于权重的策略允许使用可用的引导信息，该信息可以通过使用最先进的基于学习的算法来获得。所提出的贝叶斯模型及其估计算法的合成和真实的图像进行验证，显示竞争力的结果，关于推理的质量和计算复杂性相比，国家的最先进的算法。
3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e.g., imaging through obscurants). This paper presents a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in such environments. The algorithm exploits multi-scale information to provide robust depth and reflectivity estimates together with their uncertainties to help with decision making. The proposed weight-based strategy allows the use of available guide information that can be obtained by using state-of-the-art learning based algorithms. The proposed Bayesian model and its estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.