3 research outputs found
3D Target Detection and Spectral Classification for Single-photon LiDAR Data
3D single-photon LiDAR imaging has an important role in many applications.
However, full deployment of this modality will require the analysis of low
signal to noise ratio target returns and a very high volume of data. This is
particularly evident when imaging through obscurants or in high ambient
background light conditions. This paper proposes a multiscale approach for 3D
surface detection from the photon timing histogram to permit a significant
reduction in data volume. The resulting surfaces are background-free and can be
used to infer depth and reflectivity information about the target. We
demonstrate this by proposing a hierarchical Bayesian model for 3D
reconstruction and spectral classification of multispectral single-photon LiDAR
data. The reconstruction method promotes spatial correlation between
point-cloud estimates and uses a coordinate gradient descent algorithm for
parameter estimation. Results on simulated and real data show the benefits of
the proposed target detection and reconstruction approaches when compared to
state-of-the-art processing algorithm