Deploying 3D single-photon Lidar imaging in real world applications faces multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based frameworks. Statistical methods provide rich information about the inferred parameters but are limited by the assumed model correlation structures, while deep learning methods show state-of-the-art performance but limited inference guarantees, preventing their extended use in critical applications. This paper unrolls a statistical Bayesian algorithm into a new deep learning architecture for robust image reconstruction from single-photon Lidar data, i.e., the algorithm's iterative steps are converted into neural network layers. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling effects, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.
翻译:在现实世界应用中部署3D单光子激光雷达成像面临多种挑战,包括在高噪音环境中的成像。提议了几种算法,以根据统计或学习框架解决这些问题。统计方法提供关于推断参数的丰富信息,但受假设模型相关结构的限制,而深层次学习方法则显示最先进的性能,但推断保证有限,防止其在关键应用中被扩大使用。本文将一个统计巴伊西亚算法注入一个新的深层次学习结构,从单发光子激光雷达数据中进行稳健的图像重建,即将算法的迭代步骤转换为神经网络层。基于统计和学习框架的优势带来了算法效益,提供了最佳的估计数,提高了网络解释性。与现有的基于学习的解决办法相比,拟议的结构需要减少可培训的参数数量,对噪音和错误建模效应更为有力,并提供了关于包括不确定性计量在内的估计数的更丰富信息。合成和真实数据的结果显示,在与状态算法相比,推断质量和计算复杂性方面具有竞争性结果。