Robust Compressive Histogramming Based on Autoencoder for SPAD Direct ToF LiDAR Covering Challenging Scenarios

The high-resolution single-photon avalanche diode (SPAD) sensor array chip for light detection and ranging (LiDAR) faces a challenge in handling high data rates, due to the use of time-correlated single photon counting (TCSPC). On-chip partial histogramming methods suffer from low compression ratio (CR) and high latency. Count/histogram-less methods are with high noise-sensitivity. Direct compression on received photons has achieved high CRs but relies on handcrafted analytical codebooks and lacks of robustness. This paper proposes a robust and data-driven compressive histogramming method based on the autoencoder, which shows superior accuracy improvement over previous compression methods, capable of covering challenging imaging scenarios. Furthermore, the compact 4-bit quantized compression engine design that can process at least 64 timestamps per illumination is proposed and implemented in FPGA. A line scanning LiDAR system is constructed after connecting the engine with our previous SPAD array chip. Compared to the TCSPC-based full histogram circuit, a memory size reduction of 3.76× is achieved while maintaining similar depth accuracy. Compared to the sweep-based partial histogramming circuit, the proposed design achieves a 4× improvement in CR, and a 65% reduction in Root Mean Square depth Error (RMSE).