ToF Estimation Based on Compressed Real-Time Histogram Builder for SPAD Image Sensors

Abstract

This paper presents a FPGA implementation of a novel depth map estimation algorithm for direct time-of-flight CMOS image sensors (dToF-CISs) based on single-photon avalanche-diodes (SPADs). Conventional ToF computation algorithms rely on complete ToF histograms. The next generation of high speed dToF-CIS is expected to have wide dynamic range and high depth resolution. Applications such as 3D imaging based on dToF-CISs require pixel-level ToF histograms which have to be stored by huge fully-random access memory (RAM) modules. The proposed shifted inter-frame histogram (SiFH) algorithm has the same accuracy but requires a memory footprint 128 times smaller than the conventional algorithm.Thus a much larger number of pixels can be resolved using limited block RAM resources of FPGAs. Moreover the overall frame rate is also remarkably improved compared to the scanning method. The proof of concept of the SiFH algorithm on 15 bits has been implemented on Spartan-3E. An automated testbench was developed to confirm that no ambiguity errors occur along the entire dynamic range.

Numerical Model of SPAD-Based Direct Time-of-Flight Flash LIDAR CMOS Image Sensors

“…The distance measurement is then extracted from the histogram after a sufficient number of acquisitions are obtained, in order to increase the signal to noise ratio. In order to increase the data throughput, more complex solutions can be foreseen and tested, such as multiple-event direct histogramming [ 27 , 28 ] and histogram compression techniques [ 29 ]. Additionally, it is also possible to evaluate the most suitable distance extraction algorithm, such as peak detection, centroid-based estimation [ 13 ], or pulse correlation [ 30 , 31 ].…”

Partial‐histogram design without frame rate and data loss based on gray code peak searching

“…However there is data loss if target distance spans over multiple coarse bins. Date loss can be avoided by overlaps at a cost of area [5]. Also, "zooming" approaches typically have low signal-to-noise ratio (SNR) because of the large built-up of background counts.…”

“…And all the "zooming" approaches suffer from frame rate loss caused by step-by-step zooming in refs. [3][4][5][6][7]. For example, method in ref.…”