基于粗到细样条草图的存储与带宽高效SPAD-LiDAR测距

中文摘要 AI 辅助

本文针对直接飞行时间（dToF）光探测与测距（LiDAR）提出了一种优化的压缩框架，采用精确、紧凑的时间戳编码策略，以解决单光子雪崩二极管（SPAD）阵列中单光子时序信息带来的高数据率瓶颈。我们提出了一种硬件友好的时间戳到深度框架，其中一种稀疏单光子编码策略，即粗到细样条草图（CFSS），将光子时间戳投影为细粒度、低维的表示，称为草图值。CFSS将时间戳实时转换为草图值，并以较低的内存消耗进行累积，无需构建直方图，从而节省片上/设备内存。CFSS用于飞行时间（ToF）检索的闭式解确保了无需迭代的重建。与传统的草图化LiDAR框架相比，所提方法在单峰情况下保持相同的压缩比，压缩比范围从数百倍到数千倍，具体取决于精度与复杂度的权衡，同时提高了深度估计精度。合成数据集和真实数据集均用于验证性能。CFSS框架进一步扩展到双峰场景，能够恢复被部分遮挡的伪装物和均匀散射的半透明介质所遮挡的物体。还通过明确分离在线硬件处理与离线软件工作负载，研究了固件实现。

英文摘要

This work presents an optimized compression framework for direct time-of-flight (dToF) light detection and ranging (LiDAR), using an accurate, compact timestamp-encoding strategy to address the high data-rate bottleneck from single-photon timing information in single-photon avalanche diode (SPAD) arrays. We propose a hardware-friendly timestamp-to-depth framework in which a sparse single-photon encoding strategy, namely coarse-to-fine spline sketches (CFSS), projects photon timestamps into fine-grained, low-dimensional representations, called sketch values. CFSS converts timestamps on-the-fly to sketch values and accumulates them with lower memory consumption, without constructing histograms, thereby saving on-chip/device memory. CFSS's closed-form solution for ToF retrieval ensures iteration-free reconstruction. Compared with the conventional sketched-LiDAR framework, the proposed method retains the same compression ratio in the single-peak case, ranging from hundreds$\times$ to thousands$\times$ depending on the accuracy-complexity trade-off, while improving depth-estimation accuracy. Both synthetic and real datasets are used to validate the performance. The CFSS framework is further extended to two-peak scenarios, enabling recovery of objects obscured by partially occluding camouflage and uniformly scattering semi-transparent media. Firmware implementation is also investigated by clearly separating online hardware processing from offline software workloads.

发表机构

• University of Edinburgh（爱丁堡大学）

机构由 AI 辅助整理，请以论文原文为准。