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.
翻译:3D Lidar成像在使用多波长或高噪音环境中的成像(例如通过隐蔽剂成像)时可能是一种具有挑战性的模式,本文件介绍了一种在这种环境中强有力地重建多光谱单发利达尔数据的一种等级的巴伊西亚算法,该算法利用多尺度信息提供稳健的深度和反射性估计及其不确定性,以帮助决策。拟议的基于加权的战略允许使用现有指导信息,而这些信息可以通过使用最先进的基于学习的算法获得。 拟议的巴伊西亚模型及其估计算法在合成图像和真实图像上得到验证,显示与最新算法相比,在推断质量和计算复杂性方面的竞争性结果。