Luminance-Depth Reconstruction From Compressed Time-of-Flight Histograms

Single photon avalanche diodes (SPADs) combined with high-frequency time-to-digital converters (TDCs) enable the estimation of photon Time-of-Flight (ToF) for active 3D-depth imaging. Nevertheless, SPAD sensors still face hardware limitations due to a complex pixel readout design and a large amount of data collected by way of pixel-wise histograms. The intrinsic high background illumination (BI) also remains a challenging issue for the related depth reconstruction algorithms. Using a physically-relevant SPAD sensor model, this work tackles these issues by implementing a pixel-wise ToF histogram compressive sensing (CS) with a specific deep generative model based reconstruction. It demonstrates a possible reduction of hardware design constraints while reaching a depth inference root mean square error below 16 centimeters regardless of BI (50–1050
$\mathrm{W}\mathrm{/}{\mathrm{2}}^{2}$
) and distance (20
$\mathrm{m}$
), at a compression ratio (CR) of 10% (32 CS measurements). In addition, this paper introduces a novel multimodal reconstruction from SPAD data, enabling joint depth and luminance estimations. Indeed, since ToF histogram raw data gathers multiple physical scene characteristics, we propose a two-part deep generative model (DGM) capable of inferring Super-Resolved depth maps and normalized luminance images, independently from the average scene BI. Our key contributions related to the DGM topology design are the introduction of proper normalization layers with a learned pile-up effect compensation, multidimensional-multiscale filtering and the concatenation of Softmax-ReLU activation functions to capture both peak-position and relative amplitude features. Numerically, depth and luminance maps reconstructions of natural scenes respectively reach more than 30 dB and 25 dB PSNRs for any CR higher than 2.5%.