reference surfaces using the Leica Axyz ([3]) software (Figure 
9). 
  
(1) 
  
Q) 
Figure 9: Point offsets from CMM derived surfaces 1.2 
4.4.1 Laser dot data 
In comparing the laser data to the CMM surface 1, the average 
offset from the reference surface is of the order of twice the 
estimated target coordinate precision (Table 3). The data also 
demonstrates a small negative bias indicating more points are 
below the reference surface than above it. 
Positive 
(mm) Negative 
  
Table 3: Surface 1 to laser dot point cloud comparison results 
(42 points) 
4.4.2 Pattern projection data 
The pattern projection data set provided a more dense surface 
than the laser points for surface 1 (Table 4, 5), however the 
distribution of surface coverage is limited to a particular region. 
In this case though, the data are evenly distributed either side of 
the surface. 
(mm) Positive 
Negative 
  
Table 4: Surface 1 to pattern projection data comparison results 
(132 points) 
Measurement of surface 2 was hampered by several low 
intensity patterns, which in addition to shadow and occlusion 
effects cased by the projector and camera geometries, have 
resulted in only raised areas of surface being successfully 
measured. The data exhibit significant negative bias and a 
particularly large discrepancy between precision and accuracy 
estimates. 
(mm) Positive 
Negative 
  
Table 5: Surface 2 to pattern projection data comparison results 
(97 points) 
4.4.3 White light data 
The data set with the projected white light produced point 
clouds that covered a greater portion of each surface (Tables 6, 
7). This data set exhibits larger maximum discrepancies with 
respect to the reference surfaces, which are probably 
attributable to regions of lower image texture. 
(mm) Positive 
Negative 
  
Table 6: Surface 1 to light projection data comparison results 
(104 points) 
Positive 
(mm) Negative 
  
Table 7: Surface 2 to light projection data comparison results 
(106 points) 
4.44 Accuracy summary 
The accuracy assessment for these data sets is relying on the 
fidelity of the reference surface, which has been interpolated 
from the CMM point data by means of B-spline surfaces. Such 
a method is appropriate for smooth surfaces but can have 
unpredictable and  non-quantifiable effects for complex 
surfaces. Also to be considered is the fact that the gearbox 
surface in the areas tested has a surface texture resulting in an 
increase in CMM probe measurement uncertainty. 
All three data sets demonstrate agreement with the reference 
surface to the order of 0.3 to 0.4 mm. There is a noticeable 
negative bias in all cases, the cause of which cannot be 
ascertained from the information available but may be linked to 
limitations in the reference surface definition. 
5. CONCLUSIONS 
This paper presents some results towards the efficiency, 
accuracy and reliability of making dense automated 
measurements of complex engineering surfaces using multi 
station photogrammetry. The developed method has been 
shown to provide very dense point clouds of known quality. 
The densification process is applicable to both white light and 
artificial texture projection techniques. In both cases a dense 
point cloud has been automatically provided with very few 
outliers following filtering in the bundle adjustment. 
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