Fast adaptive scene sampling for single-photon 3D lidar images

Abderrahim Halimi (1) , Philippe Ciuciu (2, 3) , Aongus Mccarthy (1) , Stephen Mclaughlin (1) , Gerald S Buller (1)

1 HWU - Heriot-Watt University [Edinburgh]
2 NEUROSPIN - Service NEUROSPIN
3 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

Reducing acquisition time is a major challenge for single-photon based imaging. This paper presents a new approach for adaptive scene sampling allowing for faster acquisition when compared to classical uniform sampling or random sampling strategies. The approach is applied to the laser detection and ranging (Lidar) three-dimensional (3D) imaging where sampling is optimized regarding the depth image. Based on data statistics, the approach starts by achieving a robust estimation of the depth image. The latter is used to generate a map of regions of interest that informs next samples positions and their acquisition times. The process is repeated until a stopping criterion is met. A particular interest is given to fast processing to allow real-world application of the proposed approach. Results on real data show the benefits of this strategy that can reduce acquisition times by a factor of 8 compared to uniform sampling in some scenarios.

Mots clés: Adaptive sampling; 3D imaging; Single-photon Lidar; Poisson statistics; Robust estimation

Domaines: Traitement du signal et de l'image [eess.SP]; Imagerie

Abderrahim Halimi, Philippe Ciuciu, Aongus Mccarthy, Stephen Mclaughlin, Gerald S Buller. Fast adaptive scene sampling for single-photon 3D lidar images. IEEE CAMSAP 2019 - International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2019, Le Gosier (Guadeloupe), France. ⟨hal-02298998⟩