Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

Abstract. The grain-scale morphology and size distribution of
sediments are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality. In turn, characterizing the
spatial evolution of grain size and shape can help understand the dynamics
of erosion and sediment transport in coastal, hillslope and fluvial
environments. However, the size distribution of sediments is generally
assessed using insufficiently representative field measurements, and
determining the grain-scale shape of sediments remains a real challenge in
geomorphology. Here we determine the size distribution and grain-scale shape
of sediments located in coastal and river environments with a new
methodology based on the segmentation and geometric fitting of 3D point
clouds. Point cloud segmentation of individual grains is performed using a
watershed algorithm applied here to 3D point clouds. Once the grains are
segmented into several sub-clouds, each grain-scale morphology is determined
by fitting a 3D geometrical model applied to each sub-cloud. If different
geometrical models can be tested, this study focuses mostly on ellipsoids to
describe the geometry of grains. G3Point is a semi-automatic approach that
requires a trial-and-error approach to determine the best combination of
parameter values. Validation of the results is performed either by comparing
the obtained size distribution to independent measurements (e.g., hand
measurements) or by visually inspecting the quality of the segmented grains.
The main benefits of this semi-automatic and non-destructive method are that
it provides access to (1) an un-biased estimate of surface grain-size
distribution on a large range of scales, from centimeters to meters; (2) a
very large number of data, mostly limited by the number of grains in the
point cloud data set; (3) the 3D morphology of grains, in turn allowing the
development of new metrics that characterize the size and shape of grains;
and (4) the in situ orientation and organization of grains. The main limit of
this method is that it is only able to detect grains with a characteristic
size significantly greater than the resolution of the point cloud.
