Single-photon avalanche diode (SPAD) based LiDAR is becoming the de-facto choice for 3D imaging in demanding applications such as autonomous vehicles due to their improved depth resolution, sensitivity, and long ranges of operation. However, they suffer from three significant limitations: (a) the additional time-of-arrival dimension results in a data throughput bottleneck, (b) limited spatial resolution due to either low fill-factor (flash LiDAR) or scanning time (scanning-based LiDAR), and (c) course depth resolution due to quantization of photon timing by existing timing circuitries.

We propose a novel, in-pixel computing architecture that we term first arrival differential (FAD) LiDAR, where instead of recording quantized time-of-arrival information at individual pixels, we record a temporal differential measurement between pairs of pixels. The differential measurement is dependent on the relative order of photon arrivals at the two pixels (within a cycle) and creates a one-to-one mapping between this differential measurement and depth differences between the two pixels. We perform detailed system analysis and characterization using both analytical derivations, and experimental emulation using a scanning-based single-photon avalanche diode. We demonstrate several advantages of this design such as 10- 100x lower data throughput, and a greater than 10x reduction in required in-pixel chip footprint area, all the while maintaining (or in some cases improving) depth resolution and depth range.