![](bg4.png)
Table 1: Corners repeatability mean and standard error de-
viation in strong light variation for 2D and 3D Harris corner
detectors. P
i
1≤i≤3
are the input clouds. The mean error
¯
ε
2D
high values show how light affects 2D corners.
pairs P
1
− P
2
P
1
− P
3
P
2
− P
3
¯
ε
2D
177034 695510 698783
¯
ε
3D
0 0 0
σ
2D
1.54e + 06 5.42e + 06 5.81e + 06
σ
3D
0 0 0
Figure 4: Geometric corners detection: texture invariance.
Cornerness is displayed from green (weak) to red (strong).
3D cornerness - right column - is almost the same for all the
objects although they carry different textures while 2D cor-
nerness - middle column - varies according to the texture.
and normals. Detection results (non filtered) are pre-
sented in Figure 4.
The results shown on Figure 4, confirm our intu-
ition: since we rely solely on geometric data varia-
tion for this experiment, objects of the same shape
should have similar signature. Lecturers can easily
notice that 3D cornerness measure is analogous for
the whole 3
rd
column while it differs depending on
the object texture along the 2
nd
column.
5 CONCLUSIONS
This paper addresses the problem of corner detection
in RGB-D space to improve repeatability under strong
light variation or in texture-less environments. The
novelty of the proposed solution is the use of a geo-
metric criterion to assess the nature of a point. The
novel detectors are extension of popular 2D images
corner detectors: second moment matrix and self dis-
criminality ones.We prove stability of designed de-
tectors through experimental validation. Future work
include application to point cloud registration, object
recognition and tracking.
ACKNOWLEDGMENTS
This work has been supported both by the French na-
tional research agency (ANR) by the project ANR As-
sist ANR-07-ROBO-0011, and by the Willow Garage
company, Menlo Park, California, USA.
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