用视觉聚焦提升单光子3D成像效率，大幅减少内存占用。
FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging
- 通过外部信号引导传感器聚焦深度信息，实现自适应感知。
- 实测与仿真均显示内存使用降低1548倍，抗环境光能力增强。
- 适合自动驾驶等高精度实时3D感知场景，可适配新旧SPAD阵列。
快速、高效且精确的深度感知对自动驾驶等安全关键应用至关重要。直接飞行时间激光雷达（LiDAR）凭借其在远距离下提供高精度深度测量的能力，具备满足这些需求的潜力。传统激光雷达依赖雪崩光电二极管（APDs），而单光子雪崩二极管（SPADs）是一种新兴成像技术，具有极高灵敏度和时间分辨率等优势。本文解决了制约SPAD激光雷达广泛应用的关键问题：易受环境光干扰以及需处理大量原始光子数据以获得像素级深度估计。我们提出新算法与感知策略，显著提升信噪比，并提高计算与内存效率。采集时，利用外部信号实现‘聚焦’（foveate），即引导SPAD系统估计场景深度。该聚焦方法使系统能‘聚焦’于感兴趣的信号，减少需存储和传输的原始光子数据量，同时增强抗环境光能力。实验结果包括仿真与真实硬件模拟，特定实现达到1548倍的内存使用降低，且算法可应用于现有及未来的SPAD阵列。
原文摘要 · Abstract (English)
Fast, efficient, and accurate depth-sensing is important for safety-critical applications such as autonomous vehicles. Direct time-of-flight LiDAR has the potential to fulfill these demands, thanks to its ability to provide high-precision depth measurements at long standoff distances. While conventional LiDAR relies on avalanche photodiodes (APDs), single-photon avalanche diodes (SPADs) are an emerging image-sensing technology that offer many advantages such as extreme sensitivity and time resolution. In this paper, we remove the key challenges to widespread adoption of SPAD-based LiDARs: their susceptibility to ambient light and the large amount of raw photon data that must be processed to obtain in-pixel depth estimates. We propose new algorithms and sensing policies that improve signal-to-noise ratio (SNR) and increase computing and memory efficiency for SPAD-based LiDARs. During capture, we use external signals to \emph{foveate}, i.e., guide how the SPAD system estimates scene depths. This foveated approach allows our method to ``zoom into'' the signal of interest, reducing the amount of raw photon data that needs to be stored and transferred from the SPAD sensor, while also improving resilience to ambient light. We show results both in simulation and also with real hardware emulation, with specific implementations achieving a 1548-fold reduction in memory usage, and our algorithms can be applied to newly available and future SPAD arrays.
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