高通量无计数单光子3D相机
High-Flux Count-Free Single-Photon 3D Cameras
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中文总结 AI 辅助
针对SPAD单光子相机的堆积失真与数据瓶颈问题，提出结合自由运行捕获和分析合成软件流水线的计算成像方法，可在宽光照范围可靠捕获场景信息，助力其应用于高通量场景。
中文摘要 AI 辅助
基于单光子雪崩二极管（SPAD）技术的单光子相机因极高的灵敏度和时间分辨率，在3D传感领域越来越受欢迎。单光子相机存在两个限制其广泛应用的关键挑战：（i）在高光通量条件下工作时，会出现称为“堆积”的非线性失真；（ii）会产生大量原始光子数据，在每个传感器像素处造成严重的数据瓶颈。在本研究中，我们表明，虽然压缩捕获技术可成功缓解数据传输挑战，但会加剧死区时间失真的影响，因为此类技术无法保留足够的光子检测历史信息，以通过现有方法进行后处理堆积校正。我们提出了一种新的计算成像方法，将自由运行捕获与分析合成软件流水线相结合，以缓解堆积失真。我们通过硬件仿真以及全场景和单像素模拟获得的结果表明，该方法可在广泛的光照条件下可靠捕获场景距离和反射率。本研究将使带宽严重受限的高分辨率SPAD相机能够在现实世界的高通量场景中运行。
英文摘要
Single-photon cameras based on single-photon avalanche diode (SPAD) technology are gaining popularity for 3D sensing, thanks to their extreme sensitivity and time resolution. There are two key challenges with single-photon cameras that limit their widespread use: (i) they suffer from non-linear distortions called ''pile-up'' when operated in high-photon-flux conditions, and (ii) they generate a large volume of raw photon data, creating a severe data bottleneck at each sensor pixel. In this work, we show that while compressive capture techniques successfully mitigate data transfer challenges, they exacerbate the effects of dead-time distortion because they fail to retain sufficient information about the photon detection history to allow post-processing pile-up correction via existing methods. We propose a new computational-imaging method that combines free-running capture with an analysis-by-synthesis software pipeline to mitigate pile-up distortions. Our results with hardware emulations and full-scene and single-pixel simulations show that our method can reliably capture scene distance and reflectance over a wide range of illumination conditions. Our work will enable high-resolution SPAD cameras that are severely bandwidth-constrained to operate in real-world high-flux scenarios.
发表机构
- Portland State University（波特兰州立大学）
- Boston University（波士顿大学）
机构由 AI 辅助整理，请以论文原文为准。
