Abstract
Shape from Shading (SFS) is a fundamental task in computer vision. By given information about the reflectance of an object’s surface and the position of the light source, the SFS problem is to reconstruct the 3D depth of the object from a single grayscale 2D input image. A modern class of SFS models relies on the property that the camera performs a perspective projection. The corresponding perspective SFS methods have been the subject of many investigations within the last years. The goal of this chapter is to give an overview of these developments. In our discussion, we focus on important model aspects, and we investigate some prominent algorithms appearing in the literature in more detail than it was done in previous works.
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Appendices
Appendices
Appendix 1
The irradiance equation
is reformulated as
and further simplified as
By distributing the \(I^2_{0}\), on rewrites (2.45) as
Rearranging (2.46) while factoring out \(z^2_{0}\) and \(z_0\) results it to be written as
and finally the image irradiance equation is reformulated in the form of the quadratic equation
Appendix 2
In this case, the term \(\left( P_a-P_0\right) ^\top \) is redefined as
restricting the wave-front to only propagate from the direction of \(P_b\), namely \(\left( P_a-P_0\right) \cdot \left( P_b-P_0\right) =0\), leading the derivation of the normal N to proceed as
Now by letting
one writes (2.50) as
as the normal vector to the surface point \(P_0\) in case of the degenerated case \(\eta _1=+\infty \).
Appendix 3
Because \(\left( u_b,v_b\right) \) is a neighbor of \(\left( u_0,v_0\right) \), one can write \(x_b\) and \(y_b\) as
and
with \(\left( \varDelta _1,\varDelta _2\right) \in \left\{ \left( 0,\pm 1\right) ,\left( \pm 1,0\right) \right\} \), and substitute them into the numerator of the irradiance image
to get
that is expanded as
In addition, by factoring \(f^2\left( z_b-z_0\right) \) from the first two terms of the (2.54) denominator, we have
that is more simplified in its denominator as
Taking both sides of (2.55) to the power of 2, we are lead to
letting us to have the image irradiance equation as
Now, all terms are taken to the same side
and further simplified based on the common factor \(\left( z_b-z_0\right) \) as
To this end, once again the terms \(x_b\) and \(y_b\) in (2.56) need to be replaced by (2.52) and (2.53) as
and once again rearranged based on \(\left( z_b-z_0\right) \) and \(\left( z_b-z_0\right) ^2\) as
or
that leads to
and finally written as a quadratic equation
with below coefficients\(:\)
Appendix 4
Steps in the direction of normal vector derivation by Tankus et al. [54]\(:\)
Based on (2.58), the unit normal vector is found as
Appendix 5
Steps to derive the image irradiance equation (2.37) proposed by Tankus et al. [54] and based on (2.34), (2.35) and (2.36).
Appendix 6
Steps to further simplify the image irradiance equation (2.37) of Tankus et al. [54] to the form shown in (2.38) proceeds by letting both sides of (2.37) to the power of 2 as
and rearranging it as
Taking all the terms to the left side
that simplifies to
by canceling the square root and finally appears as
Appendix 7
The expanded forms of \(\alpha _1\), \(\alpha _2\) and \(\alpha _3\) are provided as\(:\)
-
\(\alpha _1:\)
$$\begin{aligned} \begin{aligned} \alpha _1&{\mathop {=}\limits ^{}} I^2\Vert L\Vert ^2f^2\left( p^2+q^2\right) \\&{\mathop {=}\limits ^{}} I^2\Vert L\Vert ^2f^2p^2 + I^2\Vert L\Vert ^2f^2q^2 \end{aligned} \end{aligned}$$ -
\(\alpha _2:\)
$$\begin{aligned} \begin{aligned} \alpha _2 {\mathop {=}\limits ^{}}&I^2\Vert L\Vert ^2\left( up+vq+1\right) ^2 \\ {\mathop {=}\limits ^{}}&I^2\Vert L\Vert ^2\left( u^2p^2+v^2q^2+1+2pquv+2up+2vq\right) \\ {\mathop {=}\limits ^{}}&I^2\Vert L\Vert ^2u^2p^2+I^2\Vert L\Vert ^2v^2q^2+ I^2\Vert L\Vert ^2+ \cdots \\ {}&2I^2\Vert L\Vert ^2pquv+2I^2\Vert L\Vert ^2up+2I^2\Vert L\Vert ^2vq \\ \end{aligned} \end{aligned}$$ -
\(\alpha _3:\)
$$\begin{aligned} \begin{aligned} \alpha _3 {\mathop {=}\limits ^{}}&-\left( \left( u-fp_s\right) p + \left( v-fq_s\right) q + 1 \right) ^2 \\ {\mathop {=}\limits ^{}}&-\left( u-fp_s\right) ^2p^2 - \left( v-fq_s\right) ^2q^2 - 1 - \cdots \\ {}&-2pq\left( u-fp_s\right) \left( v-fq_s\right) -2p\left( u-fp_s\right) -2q\left( v-fq_s\right) \end{aligned}. \end{aligned}$$
Appendix 8
To derive (2.39), let us start from (2.38) and proceed as
Now, those components have the terms of interest \(p^2\), \(q^2\), 2pq, 2p and 2q in common which are marked as
that leads to
and finally to
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Breuß, M., Mansouri Yarahmadi, A. (2020). Perspective Shape from Shading. In: Durou, JD., Falcone, M., Quéau, Y., Tozza, S. (eds) Advances in Photometric 3D-Reconstruction. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-51866-0_2
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