# 3. Passive Shape Recovery

Sonka Ch 9, Ch 10 sec 1., Trucco Ch 9.

Having developed the basic 3-D imaging models, we will turn here to applying them to shape recovery, i.e. determining the range z(x,y) for each (x,y) in the image. Various cues can be used to do this: stereo disparity (shape-from-stereo), radiometric variations over the image plane (shape-from-shading), appearance of surface textures (shape-from-texture) and others.

We begin with shape-from-stereo. The first, and hardest, problem in using shape-from-stereo is solving the so-called correspondence problem, which we must discuss first.

## The correspondence problem

Stereopsis can be used to find the range of any scene point X as long as X is visible in both images. So, logically, the use of stereoptic processing requires two steps:

1. Given an image point $$(u_l, v_l)$$ in one image corresponding to a desired scene point X, find the corresponding point $$(u_r,v_r)$$ in the other image.
2. Apply the stereopic formulas to find X: First solve for $$a_l$$ and $$a_r$$:
$$R_l^{-1}a_lK_l^{-1}[u_l,v_l,1]^T+t_l = R_r^{-1}a_rK_r^{-1}[u_r,v_r,1]^{T}+t_r$$
Then plug back into either of these to get X_w:
$$X_w = R_l^{-1}a_lK_l^{-1}[u_l,v_l,1]^T+t_l$$
$$= R_r^{-1}a_rK_r^{-1}[u_r,v_r,1]^T+t_r$$

The correspondence problem (step 1 above) is easily solved when only a single prominent feature point of an easily recognizable object is required.

• E.g.: The upper left hand corner of the roof line of the building in the stereo image pair.

Any sparse set of distinctive feature points can be easily corresponded. But difficulties arise when the set is dense, or when the features are not prominent.

• E.g.: Given a large pointset of locations on a blank wall in an image, find the corresponding pointset in the other image.

The most general, and most difficult, case is called shape-from-stereo correspondence. This is the goal of finding, for each point (u,v) in one image, the corresponding point in the other. This allows us to compute $$X_w$$ for every visible scene point, i.e. it completely solves the recovery problem.

Constraints which help reduce the computational complexity of the correspondence problem

1. The epipolar constraint: the corresponding point must lie along the epipolar line. Converts a 2-D search to 1-D search.
2. Similarity constraints: corresponding points must be similar with respect to:
1. brightness: photometric compatibility constraint;
2. geometric properties: geometric similarity constraint;
3. edge occupancy: figural disparity constraint;
3. Set constraints: sets of correspondences typically satisfy:
1. continuity of disparity: disparity smoothness constraint;
2. ordering invariance: ordering constraint;
4. Correspondence invertibility constraints:
1. At most one corresponding point: uniqueness constraint;
2. If $$u_r$$ corresponds to $$u_l$$ , then $$u_l$$ must correspond to $$u_r$$: mutual correspondence constraint.

Some of these "constraints" should be considered hard constraints which must be satisfied exactly, others soft constraints. E.g., the epipolar is a hard constraint and photometric compatibility soft.

Note: Disparity is defined as the difference $$(u_l,v_l)-(u_r,v_r)$$ between image plane locations of corresponding points in a stereo pair of images.

## Correspondence as feature matching

The most direct approach to solving the correspondence problem over sparse pointsets is by identifying prominent features of each u to be corresponded, then searching the other image for points with identical or very similar features in the other images.

The correspondence constraints cited above can then be used to parse down the set of candidate matching points for each u.

• Features of all kinds can be used: geometric, photometric, color, texture, moments, etc.

Feature-matching can also be used for dense point-sets. The key is to have "prominent features" characterize each point. For instance, suppose all edges are extracted from both images. Starting from a prominent corner, the ordering and disparity smoothness constraints can be used to work correspondences along an edge starting at the corner.

## Correspondence as maximum correlation over a window

As the pointsets to be corresponded get denser, and the features less prominent, the feature-matching algorithms performance degrades. For instance, shape-from-stereo algorithms could be initialized by feature-matching "front ends", but not every point in an image has prominent features.

Correlation-based correspondence

for a given pointset $$U={u_1,..., u_n}$$ in the left image to be corresponded,

1. i=1;
2. center an mxm template at $$u_i$$ in the left image;
3. for each point (p,q) in the right image compute the mean-square error between the template and an mxm window centered at (p,q).
4. Designate the set of all points in the right image whose mse is within a tolerance level of the minimum mse as the $$u_i$$-correspondence candidate set.
5. Use the correspondence constraints to eliminate candidates, then select the remaining candidate with the least mse as the corresponding point.
6. If i=n stop, else increment i and return to step 2.

The basic idea of shape-from-shading is that variations in the brightness of nearby points in a scene can usually be ascribed to variations of the orientation of the surface with respect to the light source.

E.g.:

If the angles subtended by Light Rays 1 and 2 are the same, the total radiant energy striking the object is the same. But that energy is spread out over a larger surface patch for Ray 1, since the angle between the outward pointing normal and a vector pointing back at the light source is larger than for Ray 2. So if this radiant energy is reflected off the surface and captured by two pixels of equal size on the image plane, pixel 1 will see a surface patch with lower energy density striking it and so pixel 2 will be brighter.

From the brightness variation we can deduce that the OPN of the surface patch imaged at pixel 2 points back at the light source, while the normal at pixel 1, appearing less bright, must "look away" from the light source.

E.g.: Sphere with light source at camera.

The measurement of flows of radiant energy (e.g. visible light, RF, UV)
Photometry
How the human eye interprets visible light.

The detailed study of brightness and color variations in an image and how they are related to objects in the scene, their shapes, locations and surface characteristics, the scene illumination, and the properties of the focal plane array requires careful radiometric modeling. Here we can only summarize these considerations as they pertain to the problem of shape-from-shading.

We will not consider photometry at all. That is essential for understanding how humans do shape-from-shading, but our goal is to understand the general process.

## Some radiometric quantities and their definitions

Radiometry is concerned with how radiant energy is emitted from a source, distributes itself in space, is scattered by material objects, is redistributed after scattering, then strikes and stimulates a detector surface.

• Radiant flux ($$\Phi$$) is the amount of power radiated by a source. Units: Watts (W).
• Radiant energy is is the time-integral of radiant flux. Units: Joules (J).
• Solid angle ($$\theta$$) subtended by a 3-D ray can be measured by surrounding the source of the ray with a unit sphere, then measuring the surface area which is subtended by the ray, i.e. the area of the intersection of the surface and the ray. Units: steradians (sr).
• Radiance (L) is the radiant flux from an emitting surface per unit surface area and per unit solid angle. Units: $$W m^{-2} sr^{-1}$$.

• E.g.: A sphere with total surface area of 1 $$mm^2$$ radiates a radiant flux of 10 W. Then if the flux is uniformly distributed, this source produces a radiance of $$L = (10 W)/((10^{-6} m^2)(4 \pi sr))= 795.8 kW m^{-2} sr^{-1}$$
• Irradiance (E) is the power (radiant flux) per unit surface area that strikes a surface in the presence of radiant energy.

So a radiant source emits a spatial distribution of radiance L(x,y,z) out into the scene. The source might be a light bulb, or it might be a reflecting surface. A detector receives some amount of this radiance at the point (u,v) on the image plane as irradiance E. The brightness of the pixel at (u,v) depends upon E. Note that for a given radiance L at a point (x,y,z), the irradiance of a surface point there will depend on its angle relative to the direction of the radiant ray.

• E.g.: Our light source in the previous example illuminates a 1cm x 1cm film located 5m away. What is the irradiance on the film?

The source produces

$$795.8*10^3*10^{-6} = 0.795 W sr^{-1}.$$

The 1cm x 1cm film located 5m away subtends a solid angle of approximately

$$\theta /(4*\pi) = 10^{-4} m^2 / (4 \pi 5^2 m^2) = 4.0 \mu sr$$

$$0.795 W/sr * 4.0 \mu sr = 3.18 \mu W$$

$$E = 3.18 \mu W / 10^{-4} m^2 = 31.8 mW/m^2$$

Note that if the film were oriented at an angle of say 45 with respect to the radiant ray, then the solid angle subtended by the film at the source drops by a factor of cos 45, and so the irradiance will be reduced to .707 times the calculated value above.

This figure is a cross section showing that the film patch height on a sphere surrounding the source will be reduced by factor of 0.707 when tipped 45 subtended by the film when viewed at the source is decreased by the same factor.

For this 45-"tipped" film surface, its irradiance is now

$$E = 0.707*31.8 mW/m^2 = 22.5 mW/m^2$$

## Reflectance

The power which is reflected from a surface per unit surface area into a given solid angle depends on several factors: the irradiance at the surface, the surface orientation relative to the ray carrying that irradiance towards the surface, and the surface characteristics.

## Surface orientation

For each point (x,y) in the image, let z(x,y) specify the z-coord of the surface point in the scene visible at (x,y) in camera coordinates. Then let us define the surface gradient vector $$[p,q]^T$$ as the vector of partial derivatives

$$[p,q]^T = [\partial z/\partial x, \partial z/\partial y]^T$$

The 3-D vectors $$[1,0,p]^T$$ and $$[0,1,q]^T$$, in the xz and yz planes respectively, point tangent to the surface, hence a vector perpendicular to both must be a normal to the surface. One such is the vector cross-product of these two vectors $$[-p,-q,1]^T$$. (p,q) is called gradient space, and gradient space specifies the surface orientation.

## Surface characteristics

Surfaces reflect a specific fraction of their irradiance, called the albedo. That irradiance equals the reflected radiance distributed through a $$2\pi$$ solid angle centered about the surface normal.

If the reflected radiance is uniform in that solid angle, the surface is said to be Lambertian, and the reflectance (reflected radiance) satisfies

$$R = \rho \cos \theta_i / \pi$$

where $$\theta_i$$ is the angle of incidence (angle between the normal and the incoming irradiance ray) and $$\rho$$ is the albedo.

In general, R depends on $$\rho$$, $$\cos \theta_i$$ and on the difference between the angle of incidence and the angle whose R is being computed (angle of reflection or view angle). The angle of reflection does not matter only for pure Lambertian surfaces with no specular component. This is the only case we will consider here, the equation for R gets complicated when the specular component is considered also.

## Specular vs. Lambertian

A pure specular surface is one whose reflectance R is zero except for that angle of reflection equal to the angle of incidence. Such surfaces have a "mirror" or shiny quality. Lambertian surfaces, on the other hand, have a matte or dull surface luster.

Pure specular and pure Lambertian surfaces are just limiting cases. Real surfaces in general have reflectance functions R which are neither uniform nor non-zero in just one direction.

The basic radiometric conservation equation is that which says the irradiance E received at a point (x,y) on the image plane equals the radiance R emitted at that point from the corresponding object point which is being imaged. In simplest form (see Sonka p 493-494, Trucco p 223-224)

$$E(x,y) = R(p(x,y),q(x,y)) = R(\partial z/\partial x,\partial z/\partial y)$$

where z=z(x,y) is the depth of the surface point.

This form of the irradiance equation assumes, among other things, that the radiance function depends only on surface orientation (Lambertian assumption), that the light sources and image plane are distant from the scene, and so perspective projection is well characterized by orthographic projection, i.e. image coordinates (u,v) equal the camera coordinates (x,y).

E.g.:

Orthographic projection means that the image is projected onto the x-y image plane in the "usual" way we think of projection of a 3D point onto a 2D plane, $$[x,y,z] > [x,y,0]$$.

Note that using orthographic projection, the image plane location $$[u,v]^T = [x,y]^T$$ does not depend on z. With perspective projection,as in our pinhole model, the projection of an object point [x,y,z] into the image plane depends not just on x and y, but z as well.

Then continuing with the present example, for each point on the surface of the parabaloid,

$z(x,y) = x^2+y^2+10$ $p(x,y) = dz/dx = 2x; q(x,y) = dz/dy = 2y$

Lets determine R(p(x,y),q(x,y)). Assuming Lambertian surface, $$R = \cos \theta_i / \pi$$ where $$\theta_i$$ is the angle between the surface normal and source ray.

$n = [-p,-q,1]^T = [-2x,-2y,1]^T$

But the cosine of the angle between a pair of vectors equals their inner product divided by the product of their norms, so

$\cos \theta_i = \frac{[-2x,-2y,1][1,0,0]^T}{|[-2x,-2y,1]| |[1,0,0]|}$

$E(x,y) = \frac{2x}{\pi(4x^2+4y^2+1)^{1/2}}$

Thus for instance, the brightness E on the image plane would be zero along the y-axis (why?) and go asymptotically to its max for large x (why?).

Solving the image irradiance equation IIE E(x,y)=I(p,q) for p(x,y) and q(x,y)

Given the radiance E(x,y) over an image, the image irradiance equation E(x,y)=R(p(x,y),q(x,y)) can be used to determine (or estimate) p(x,y) and q(x,y). If you know the orientation of the tangent plane to the surface everywhere, you know the shape.

There are two classes of methods used for solving the IIE, which is a partial differential equation. The Characteristic strip method is a classical pde-solver, while the optimization method works on the principle of finding a function which is a good global fit to the data.

## Characteristic strip method:

Starting from a point (x,y) where z, p and q are known, pick a path l and solve

$$dx/dl=R_p, dy/dl=R_q$$

$$dz/dl=pR_p+qR_q$$

$$dp/dl=E_x, dq/dl=E_y$$

Known points include occluding boundary and max / min points of brightness, where patch orientation is perpendicular to / parallel to light ray. For specular surfaces the specular point in the image has a known surface orientation.

More details are in

## Optimization method:

Given the Irradiance map E(x,y), an given the Reflectance map R(p,q) (but not knowing the (p,q) values as functions of (x,y)), find the functions p(x,y) and q(x,y) which minimize the sum over the entire image of the energy function

$$[E(x,y)-R(p,q)]^2+ \lambda [(\nabla^2p)^2+(\nabla^2q)^2]$$

Here $$\nabla^2$$ is the Laplacian operator (sum of squares of the partials with respect to x and y). It is a frequently-used measure of the irregularity of a function. This minimization is a variational calculus problem which can be solved via relaxation methods.

Once p(x,y) and q(x,y) are known over the entire image, since the surface gradient is defined by

$$[p,q]^T = [dz/dx, dz/dy]^T$$

then taking a single fixed depth reference point $$(x_0,y_0,1)$$ we can integrate outward over the image from that point to recover normalized z(x,y).

Now what can we say about the effectiveness of the shape-from-shading algorithms? The problem is in general ill-posed, i.e. has more degrees of freedom than constraints induced by shading of the image. Ill-posed problems can be regularized by addition of hard constraints, for instance limiting the surface characteristics or the types of objects being imaged (blocks-world approach) and/or by addition of soft constraints (optimization approach using irregularity as soft constraint).

Problem is whether the regularized problem becomes artificially over-constrained.

## Other shape-from algorithms

Stereo and shading are not the only cues that can be used to infer local surface orientation and depth distribution.

Shape-from-texture

Consider the following image:

It is hard to avoid the perception that the surface shown tips down to the left and forward. Why do we have that perception?

A texel is the basic repeating pattern of a 2-D texture. A texture is any 2-D periodic image or portion of an image.

E.g.: In the binary image

000010000100001000010000100001000010000100001
100101001010010100101001010010100101001010010
000010000100001000010000100001000010000100001
100101001010010100101001010010100101001010010
000010000100001000010000100001000010000100001
100101001010010100101001010010100101001010010
.....


the texel is

00100
01010


The texture has 2-D periodicity (2,5). Note that the texel for a given periodic image is defined only up to arbitrary horizontal and vertical phases.

• E.g. shifting the horizontal phase by 2 pixels, the texel for the previous example can be written
10000
01001


or shifting horizontal phase by 2 and vertical phase by 1, can also be written.

01001
10000


The eye tends to assume that once a texel has been identified, distortions to its shape are due to changes in the surface orientation of the surface, or due to perspective projection.

• E.g.: Suppose we have identified a texel as a square but at one point in the images, one of these texels looks like a trapezoid in perspective projection. We can infer the plane of the surface normal.

• E.g.: A square mesh distorted by the shape of a function into a "wire-mesh 3-D graph." The function is the sinc function.

$$z(x,y)=\sin (d(x,y)) / d(x,y)$$

where

$$d(x,y) = \sqrt{(x^2+y^2)}$$

Shape-from-texture algorithms work by determining the undistorted texel shape, then matching each texel in the image to that shape modified by a distortion function which reveals the surface orientation. It can be done discretely, texel by texel, or continuously, computing rate of change of surface gradient vector.

• E.g.: A square texel would look like one of the texels two images ago if its surface normal was approximately $$[1, 1, -1]^T$$.

Sonka does not give any actual algorithms, see Trucco STAT-SHAPE_FROM_TEXTURE p. 240-241 for one.

## Shape-from-photometric-stereo

For a Lambertian surface and using orthographic projection, we showed that

$$R = \rho (\cos \theta_i / \pi)$$

where $$\rho$$ was the albedo, and $$\theta_i$$ the angle between light source and surface normal. Rewrite as

$$R = \rho (\vec{L} \hat{n})$$

where $$\vec{L}$$ is a vector whose direction is that of the light ray and magnitude is the strength of the irradiance, and $$\hat{n}$$ is the unit surface normal vector. Note the factor of $$1/\pi$$ is subsumed in $$\vec{L}$$.

For a single fixed surface point (x,y) suppose we take 3 images with three different L-vectors, i.e. using three different lighting conditions:

$$E_1(x,y) = \rho (\vec{L_1} \hat{n})$$

$$E_2(x,y) = \rho (\vec{L_2} \hat{n})$$

$$E_3(x,y) = \rho (\vec{L_3} \hat{n})$$

Here $$E_i$$ are the three measured irradiances at the same point (x,y) in the image plane under the three different lighting conditions. Then we can re-form these 3 scalar equations into a single vector-matrix equation

$$\vec{E}/\rho = \mathbf{L} \hat{n}$$

where $$\vec{E} = [E_1,E_2,E_3]^T$$, and the 3x3 matrix $$\mathbf{L}$$ has $$L_1$$, $$L_2$$ and $$L_3$$ as its rows. Then

$$n = (1/\rho) \mathbf{L}^{-1} \vec{E}$$

This is shape-from-photometric stereo. Note that if >3 images are available, can use least squares fit to over-defined equations (more robust).

Limitations with shape-from-photometric-stereo as present here is the perfect Lambertian assumption and the use of orthographic projection. Gets a lot more complicated without those idealizations.

Another concern: not very biomimetic, i.e. does not relate to any evolutionary mechanism.

## Shape-from-motion

If we watch an object go around a merry-go-round, we can perceive its 3-D shape from the many views of it which we see. Similarly, if we walk around a statue, we can recover its shape. This is shape-from-multiple-views, or shape-from-motion.

The key tool here is the Structure from motion theorem.

Structure from motion theorem: given 4 or more non-co-planar points on a rigid body as viewed in 3 or more non-co-planar orthographic projections, the 3-D displacements between the points may be determined.

Proof of the theorem involves only projective geometry, and proceeds by selecting one of the points as the origin, finding the translation of that point between images and the rotation of the rigid body around that point (see Sonka p. 510-512).

One difficulty with shape-from-motion is separating shape parameters from motion parameters. Another is the rigid body assumption. For instance, cars are rigid bodies but tractor trailer trucks are not. People are not.

Shape-from-motion can also be approached on the continuous level from optical flow, i.e. the movement in image space of grey levels. We will develop optical flow in the next section of this course, on motion analysis.

Other shape-from concepts include shape-from-focus/defocus and shape-from-contour. These are perhaps less important than shape-from-X where X={stereo, shading, texture, photometric stereo, motion}. In nature all these, with the exception of X = photometric stereo, have been shown to be important shape determination mechanisms.

## Summary remarks about shape-from methods:

1. Shape-from-stereo is a practical tool in many realistic settings.
2. Shape-from-shading works well only in structured and well-characterized settings.
3. Other shape-from methods are of limited practical value in current practice, work only for a narrow range of applications.
4. What is missing from these methods is the notion of goal-seeking behavior, view planning.