In this paper, we present an algorithm for online 3D reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliably 3D profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3D profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3D scenes and the results obtained pave the way to a new family of real-time 3D reconstruction solutions.

中文翻译：

---

根据单个单光子检测事件快速在线 3D 重建动态场景。


在本文中，我们提出了一种使用单光子探测器阵列记录的光子的单独到达时间 (ToA) 在线 3D 重建动态场景的算法。使用单光子激光雷达进行 3D 成像的主要挑战之一是在存在不可忽略的环境照明的情况下构建 ToA 直方图和可靠地重建 3D 轮廓所需的积分时间。如此长的积分时间也妨碍了使用现有技术对快速动态场景进行分析。我们提出了一种新方法，它不依赖于 ToA 直方图的构建，而是首次允许在不同像素中以并行方式在线处理各个检测事件，同时考虑动态场景的内在时空结构。采用贝叶斯方法，构建贝叶斯模型来捕获 3D 轮廓的动态，并提出基于假设密度滤波的近似推理方案，产生快速且鲁棒的重建算法，能够像通常一样有效地处理数千到数百万帧使用单光子探测器记录。使用静态和动态 3D 场景进行的一系列实验评估了所提出方法的性能，每秒能够处理数百帧，获得的结果为新的实时 3D 重建解决方案系列铺平了道路。