Graphics Reference
In-Depth Information
3D Face Modeling
Techniques
Depth from triangulation
Shape from shading
Depth from Time of Flight
Time of Flight
Multi-view
reconstruction
Static
3D face
(still 3D)
Deformable
3D face
(dynamic 3D)
Time of Flight
Photometric
stereo
Laser stripe
scanning
Structured
Light
Spacetime
stereo
Time-coded
Structured Light
From single
shot
Figure 1.1
Taxonomy of 3D face modeling techniques
in section 1.2. In static face category, the multi-view stereo reconstruction uses the
optical
triangulation
principle to recover the depth information of a scene from two or more projections
(images). The same mechanism is unconsciously used by our brain to work out how far an
object is. The correspondence problem in multi-view approaches is solved by looking for
pixels that have the same appearance in the set of images. This is known as
stereo-matching
problem. Laser scanners use the
optical triangulation
principle, this time called
active
by
replacing one camera with a laser source that emits a stripe in the direction of the object to
scan. A second camera from a different viewpoint captures the projected pattern. In addition
to one or several cameras, time-coded structured-light techniques use a light source to project
on the scene a set of light patterns that are used as
codes
for finding correspondences between
stereo images. Thus, they are also based on the
optical triangulation
principle.
The moving face modeling category, unlike the first one, needs fast processing for 3D
shape recovery, thus, it tolerates scene motion. The structured-light techniques using one
complex pattern is one solution. In the same direction, the work called
Spacetime faces
shows
remarkable results in dynamic 3D shape modeling, by employing random colored light on the
face to solve the stereo matching problem. Time-of-flight-based techniques could be used to
recover the dynamic of human body parts such as the faces but with a modest shape accuracy.
Recently, photometric stereo has been used to acquire 3D faces because it can recover a dense
normal field from a surface. In the following sections, this chapter first gives basic principles
shared by the techniques mentioned earlier, then addresses the details of each method.
1.2 Background
In the projective pinhole camera model, a point
P
in the 3D space is imaged into a point
p
on
the image plane.
p
is related to
P
with the following formula:
p
=
MP
=
KR
[
I
|
t
]
P
,
(1.1)
where
p
and
P
are represented in homogeneous coordinates,
M
is a 3
×
4 projection matrix,
and
I
is the 3
×
3 identity matrix.
M
can be decomposed into two components: the intrinsic
