图注意力驱动的贝叶斯深度展开用于双峰单光子激光雷达成像
Graph Attention-Driven Bayesian Deep Unrolling for Dual-Peak Single-Photon Lidar Imaging
AI总结：
本文提出一种图注意力驱动的贝叶斯深度展开算法，用于双峰单光子激光雷达成像，结合统计可解释性与深度学习精度，在合成和真实数据上表现优异并提供不确定性。
摘要：
单光子激光雷达成像凭借其高分辨率和远距离能力，在三维成像中具有显著优势，然而在多目标且每像素存在噪声的环境中应用颇具挑战。为应对这些挑战，已有多种方法被提出。统计方法在推断参数上具有可解释性，但往往在处理复杂场景时能力有限。基于深度学习的方法在精度和鲁棒性方面表现出优越性能，但缺乏可解释性或仅限于每像素单峰。本文提出了一种用于双峰单光子激光雷达成像的深度展开算法。我们引入了一个针对多目标的层次贝叶斯模型，并提出一种神经网络来展开底层统计方法。为支持多目标，我们采用双深度图表示，并利用几何深度学习从点云中提取特征。所提方法在精度和不确定性量化方面兼具统计方法和学习方法的优势。在合成数据和真实数据上的实验结果表明，与现有方法相比，该方法具有竞争力的性能，同时还能提供不确定性信息。
英文摘要：
Single-photon Lidar imaging offers a significant advantage in 3D imaging due to its high resolution and long-range capabilities, however it is challenging to apply in noisy environments with multiple targets per pixel. To tackle these challenges, several methods have been proposed. Statistical methods demonstrate interpretability on the inferred parameters, but they are often limited in their ability to handle complex scenes. Deep learning-based methods have shown superior performance in terms of accuracy and robustness, but they lack interpretability or they are limited to a single-peak per pixel. In this paper, we propose a deep unrolling algorithm for dual-peak single-photon Lidar imaging. We introduce a hierarchical Bayesian model for multiple targets and propose a neural network that unrolls the underlying statistical method. To support multiple targets, we adopt a dual depth maps representation and exploit geometric deep learning to extract features from the point cloud. The proposed method takes advantages of statistical methods and learning-based methods in terms of accuracy and quantifying uncertainty. The experimental results on synthetic and real data demonstrate the competitive performance when compared to existing methods, while also providing uncertainty information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
