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(a) The blue UAV-MVS points amongst the T-MVS points from
above.
(b) The blue UAV-MVS points beneath the T-MVS points from below.
Figure 7: A 6 cm wide strip (profile A) of the UAV-MVS point
cloud viewed with the T-MVS point cloud.
The wider 6 cm profile strips have been created as surfaces to as-
sess the difference between UAV-MVS Poisson and T-MVS Pois-
son. Figure 3 shows the TIN surface compared to the Poisson
surface for T-MVS and UAV-MVS datasets respectively.
In these views the natural coloured point clouds have been offset
in the Z dimension by -10 cm to allow visualisation of the shape
of the surface compared to the cloud that it was derived from. The
surface covers a vegetated section and in the UAV-MVS dataset
the denser section previously mentioned can be seen when com-
paring this view (Figure 8(a)) to the same view of the shrub (Fig-
ure 8(b)). The T-MVS cloud is sparse here and as a result the
Poisson surface seems to have exaggerated the shrub height over
the sparse section, probably due to the orientation of the normals
varying greatly for those few points, which happens in vegeta-
tion. The triangulated mesh is much more jagged than the Poisson
surface in both views and the UAV-MVS Poisson is particularly
smooth (Figure 8(a)). The shrub in reality does have a reasonably
smooth shape, in in this instance the UAV-MVS Poisson appears
most accurate. To examine this further a section of Profile B that
passes through the pebbly beach is visualised. In Figure 9(b) the
Poisson surface is again smoother and the drop in terrain at this
point point is well represented (see Figure 6(b)). In Figure 9(a)
the same seems evident. In Figure 9(c) the Poisson surfaces for
UAV-MVS and T-MVS are shown on the T-MVS point cloud (Z-
10 cm). In this view the UAV-MVS surface is again ~1 cm below
the T-MVS surface, but the shape of the terrain is basically the
same, where as when two raw MVS based TINs are compared in
the same view the outliers in the UAV-MVS data seem to cause
the surface to vary suddenly causing spikes or peaks in terrain
that are not evident in the equivalent T-MVS TIN.
These visualisations provide insight into the quality of terrain and
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
479
(a) The UAV-MVS point cloud below Poisson (blue) and TIN
(light blue) strips.
A
(b) The T-MVS point cloud below Poisson (brown) and TIN
(pink) strips.
Figure 8: 6 cm wide strips of Poisson and TIN surfaces viewed
over a vegetated section of the points clouds from which they
were derived (Z-10 cm), each natural coloured dot has a 14mm
diameter.
surface extraction possible using MVS techniques. The use of
Poisson surface reconstruction has potential advantages over tra-
ditional triangulated mesh creation. Poisson surfaces seem gen-
erally smoother and smooth surface representations are often bet-
ter when undertaking decimation, hydrological analysis, DEM
derivative extraction and vegetation and ground filtering. The
apparent outliers in the point cloud may not impact on the out-
puts from these analyses and, provided the point cloud density is
carefully monitored and taken into account when mapping sur-
face quality, the result may be more realistic for most surface
types. Some vegetated areas have complex geometry (such as
complex overlapping branches or tussock grasses) and areas with
little or no texture are going to be poorly represented and this
may impact on the Poisson reconstruction. The creation of a TIN
is still a viable option, particularly when the point cloud can be
maintained without decimation. When products with a smaller
memory footprint are required there seems to be a strong case for
using Poisson surface reconstruction to create a fairly smooth yet
detailed representation of the terrain from which lower resolution
surfaces can be extracted. The TIN surfaces appear more jagged
and these spikes in the terrain can cause erroneous height values
in a derived output.
4 CONCLUSIONS AND FUTURE WORK
This study presented a qualitative assessment of the accuracy of
point clouds derived using multi-view stereo techniques (MVS).