In this paper, we present a novel method for weak laser pulse detection by full-waveform analysis. Pulse detection is a fundamental step of processing data of pulsed laser systems for extracting features of the illuminated object. Weak laser pulses below the threshold are discarded by classical methods. For full-waveform laser scanners, the entire recording of a scene can be interpreted as a discrete waveform cuboid I[x y t]-, where the measured amplitude at each time t and each beam direction [x y] is stored. The potential information hiding in the waveform cuboid could be utilized to improve the analysis result of conventional system. The neighborhood relation given by co-planarity constraint in waveform data is analyzed. Waveform stacking technique is introduced to improve the signalto-noise ratio (SNR) of objects with poor surface response in view of mutual information enhancement. Hypotheses for planar surface of different slopes are generated and verified. Each pulse signal is assessed with respect to accepted hypotheses by a contribution measure to the local geometry. Pulse signals are finally classified according to the likelihood value by automatically thresholding. The presented method was applied to waveform data of an urban scene and showed very promising results. The pulses reflected from objects with poor surface response or partially occluded are redetected, which cannot be predicted by given geometric models based on available points.     «

In this paper, we present a novel method for weak laser pulse detection by full-waveform analysis. Pulse detection is a fundamental step of processing data of pulsed laser systems for extracting features of the illuminated object. Weak laser pulses below the threshold are discarded by classical methods. For full-waveform laser scanners, the entire recording of a scene can be interpreted as a discrete waveform cuboid I[x y t]-, where the measured amplitude at each time t and each beam direction [...     »