Low-Cost SPAD Sensing for Non-Line-Of-Sight Tracking, Material Classification and Depth Imaging

Clara Callenberg, Zheng Shi, Felix Heide, Matthias B. Hullin
ACM Transactions on Graphics 40 (4), Article 61 (Proc. SIGGRAPH 2021), 2021.


Time-correlated imaging is an emerging sensing modality that has been shown to enable promising application scenarios, including lidar ranging, fluorescence lifetime imaging, and even non-line-of-sight sensing. A leading technology for obtaining time-correlated light measurements are singlephoton avalanche diodes (SPADs), which are extremely sensitive and capable of temporal resolution on the order of tens of picoseconds. However, the rare and expensive optical setups used by researchers have so far prohibited these novel sensing techniques from entering the mass market. Fortunately, SPADs also exist in a radically cheaper and more power-efficient version that has been widely deployed as proximity sensors in mobile devices for almost a decade. These commodity SPAD sensors can be obtained at a mere few cents per detector pixel. However, their inferior data quality and severe technical drawbacks compared to their high-end counterparts necessitate the use of additional optics and suitable processing algorithms. In this paper, we adopt an existing evaluation platform for commodity SPAD sensors, and modify it to unlock time-of-flight (ToF) histogramming and hence computational imaging. Based on this platform, we develop and demonstrate a family of hardware/software systems that, for the first time, implement applications that had so far been limited to significantly more advanced, higher-priced setups: direct ToF depth imaging, non-line-of-sight object tracking, and material classification.

Supplemental Material

Code and data: GitHub repository


BibTeX Citation

@article{Callenberg2021CheapSPAD, title = "Low-Cost SPAD Sensing for Non-Line-Of-Sight Tracking, Material Classification and Depth Imaging", author = "Clara Callenberg and Zheng Shi and Felix Heide and Matthias B. Hullin", journal = "ACM Trans. Graph. (SIGGRAPH)", volume = "40", number = "4", year = "2021", publisher = "ACM" }