同时估计深度与反射率，提升单光子激光雷达动态场景重建效果
Joint Depth and Reflectivity Estimation using Single-Photon LiDAR
- 基于时间戳联合建模深度与反射率，利用二者相关性提升精度
- 在真实和合成数据上优于现有方法，实现更高质量的联合重建
- 适合需要高精度3D视觉的自动驾驶、机器人导航等动态场景应用
单光子激光雷达（SP-LiDAR）正成为远距离、高精度三维视觉任务的领先技术。在SP-LiDAR中，时间戳编码了两个互补信息：脉冲飞行时间（深度）和物体反射的光子数（反射率）。现有重建方法通常分别或顺序地恢复深度与反射率，且传统3D直方图构建主要适用于慢速或静止场景。而在动态场景中，直接处理时间戳更高效有效。本文提出一种方法，可在快速运动场景中同时恢复深度与反射率。主要贡献包括：(1) 理论分析揭示深度与反射率间的相互关联及联合估计的优势条件；(2) 提出新重建方法SPLiDER，利用共享信息增强信号恢复。在合成与真实SP-LiDAR数据上，该方法均优于现有方法，实现更优的联合重建质量。
原文摘要 · Abstract (English)
Single-Photon Light Detection and Ranging (SP-LiDAR is emerging as a leading technology for long-range, high-precision 3D vision tasks. In SP-LiDAR, timestamps encode two complementary pieces of information: pulse travel time (depth) and the number of photons reflected by the object (reflectivity). Existing SP-LiDAR reconstruction methods typically recover depth and reflectivity separately or sequentially use one modality to estimate the other. Moreover, the conventional 3D histogram construction is effective mainly for slow-moving or stationary scenes. In dynamic scenes, however, it is more efficient and effective to directly process the timestamps. In this paper, we introduce an estimation method to simultaneously recover both depth and reflectivity in fast-moving scenes. We offer two contributions: (1) A theoretical analysis demonstrating the mutual correlation between depth and reflectivity and the conditions under which joint estimation becomes beneficial. (2) A novel reconstruction method, "SPLiDER", which exploits the shared information to enhance signal recovery. On both synthetic and real SP-LiDAR data, our method outperforms existing approaches, achieving superior joint reconstruction quality.
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