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Sparsity-aware 3D ToF Sensing
  • Alvaro Lopez Paredes ,
  • Otmar Loffeld ,
  • Miguel Heredia Conde
Alvaro Lopez Paredes
Center for Sensor Systems (ZESS)

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Otmar Loffeld
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Miguel Heredia Conde
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In this work, several ToF sensing schemes which tackle the challenge of obtaining high angular resolution and long ranges in nearly real-time, with relatively simple implementation and low associated computational load, are proposed. Sliced Orthogonal Matching Pursuit (OMP) divides the spatial domain in a number of partitions which ensure an efficient projection of the scene by optimizing the inter-column coherence of the sensing matrices. The signals are preliminarily localized within the partitions and OMP is then applied to recover depth and amplitude in a refined spatial domain. The preliminary set of measurements reduces the effective domain of the signal, lowers the processing times, and improves the sensing accuracy. Several methodologies are described for the construction of the sensing matrices, such as Low-Density Parity-Check codes via Progressive Edge Growth (LDPC-PEG), and random permutations of (0,1)-binary columns generated as combinations without repetition of a fixed number of non?zero elements for each column. Furthermore, we extend the previous schemes accounting for the raising and falling edges in order to avoid any possible coincidence which may degrade the coherence. Then, the upper super-resolution limit is studied accounting for the Instrument Response Function (IRF) of the ToF sensor. Zoned APEG extends the applicability of Adaptive Progressive Edge Algorithm (APEG) to more practical illumination systems by considering several groups of signals, arising from different areas of the sensor array, during the adaptation of the sensing matrices. The signals are then individually retrieved for each pixel via OMP over the identified joint signal support.