16720-B F20 3D Reconstruction &
1. Integrity and collaboration: Students are encouraged to work in groups but each
student must submit their own work. Include the names of your collaborators in
your write up. Code should NOT be shared or copied. Please properly give credits
to others by LISTING EVERY COLLABORATOR in the writeup including any
code segments that you discussed, Please DO NOT use external code unless permitted.
Plagiarism is prohibited and may lead to failure of this course.
2. Start early! This homework will take a long time to complete.
3. Questions: If you have any question, please look at Piazza first and the FAQ page
for this homework.
4. All questions marked with a Q require a submission, for theory part please answer the
question in the writeup, for the practice part in section 3 and 4 please submit both
code, .npy file if needed and short answer if question is asked.
5. For the implementation part, please stick to the headers, variable names,
and file conventions provided.
6. Attempt to verify your implementation as you proceed: If you don’t verify
that your implementation is correct on toy examples, you will risk having a huge mess
when you put everything together.
7. Do not import external functions/packages other than the ones already
imported in the files: The current imported functions and packages are enough for
you to complete this assignment.
8. Submission: We have provided a script checkA4Submission.py which will check if
you have all the files needed for submission. The submission is on Gradescope, you
will be submitting both your writeup and code zip file. The zip file, andrewid.zip, contains your code and any results files we ask you to save. Note: You have
to submit your writeup separately to Gradescope, and include results in
the writeup. Do not submit anything from the data/ folder in your submission.
Lastly, please remember to match the writeup pages to the appropriate questions in
9. Assignments that do not follow this submission rule will be penalized up to 10% of
the total score.
10. Please make sure that the file paths that you use are relative and not absolute.
1 Theory: 3D Reconstruction (25 pts) 4
2 Theory: Photometric Stereo (10 pts, EC 15pts) 4
3 Practice: 3D Reconstruction (100 pts, EC 15 pts) 7
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Fundamental matrix estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Metric Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.4 3D Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.5 Bundle Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Practice: Calibrated Photometric Stereo (35 pt, EC 15 pts) 14
4.1 Lambertian Sphere Rendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Estimating pseudonormals and albedos . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Depth recovering from normals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Deliverables 17
1 Theory: 3D Reconstruction (25 pts)
Before implementing our own 3D reconstruction, let’s take a look at some simple theory
questions that may arise. The answers to the below questions should be relatively short,
consisting of a few lines of math and text (maybe a diagram if it helps your understanding).
Q1.1 (5 pts) Suppose two cameras fixated on a point x (see Figure 1) in space such
that their principal axes intersect at that point. Show that if the image coordinates are
normalized so that the coordinate origin (0, 0) coincides with the principal point, the F33
element of the fundamental matrix is zero.
Figure 1: Figure for Q1.1. C1 and C2 are the optical centers. The principal axes intersect
at point w (P in the figure).
Q1.2 (5 pts) Consider the case of two cameras viewing an object such that the second
camera differs from the first by a pure translation that is parallel to the x-axis. Show that
the epipolar lines in the two cameras are also parallel to the x-axis. Backup your argument
with relevant equations.
Q1.3 (5 pts) Suppose we have an inertial sensor which gives us the accurate extrinsics Ri
and ti (Figure 2), the rotation matrix and translation vector of the robot at time i. What
will be the effective rotation (Rrel) and translation (trel) between two frames at different
time stamps? Suppose the camera intrinsics (K) are known, express the essential matrix
(E) and the fundamental matrix (F) in terms of K, Rrel and trel.
Q1.4 (10 pts) Suppose that a camera views an object and its reflection in a plane mirror.
Show that this situation is equivalent to having two images of the object which are related
by a skew-symmetric fundamental matrix. You may assume that the object is flat, meaning
that all points on the object are of equal distance to the mirror (Hint: draw the relevant
vectors to understand the relationship between the camera, the object, and its reflected
2 Theory: Photometric Stereo (10 pts, EC 15pts)
As we discussed in class, photometric stereo is a physics-based method to determine the
shape of an object from its appearance under a set of lighting directions. In the below theory
Figure 2: Figure for Q1.3. C1 and C2 are the optical centers. The rotation and the
translation is obtained using inertial sensors. Rrel and trel are the relative rotation and
translation between two frames.
questions, we make certain assumptions: the object is Lambertian and is imaged with an
orthographic camera under a set of directional lights. We will look into some questions for
calibrated/uncalibrated photometric stereo and we will solve a calibrated photometric stereo
problem, in which the lighting directions are given.
Figure 3: Geometry of photometric stereo
Q2.1 Understanding n-dot-l lighting (5 pts) Explain the geometry of the n-dot-l
lighting model from Fig.3 Where does the dot product come from? Where does the projected
area come into the equation? Why does the viewing direction not matter?
Q2.2 Normals and depth (5 pts) To estimate from the normals of the actual shape,
we represent the shape of the surface as a 3D depth map given by a function z = f(x, y).
Let the normal at the point (x, y) be n = (n1, n2, n3). Explain, in your write-up, why
n is related to the partial derivatives of f at (x, y): fx = ∂f(x, y)/∂x = −n1/n3 and
fy = ∂f(x, y)/∂y = −n2/n3. You may consider the 1D case where z = f(x).
Q2.3 Extra Credit: Understanding integrability of gradients (5 pts) Consider
the 2D discrete function g on the space given by the matrix below. Find its x and y gradients,
given that the gradients are calculated as gx(xi
, yi) = g(xi+1, yj ) − g(xi
, yj ) for all i, j (and
similar for y). Let us define (0, 0) as the top left, with x going in the horizontal direction
and y in the vertical.
1 2 3 4
5 6 8 8
9 10 11 12
13 14 15 16
Note that we can reconstruct the entire of g given the values at its boundaries using gx and
gy. Given that g(0, 0) = 1. perform these two procedures.
1. Use gx to construct the first row of g, then use gy to construct the rest of g.
2. Use gy to construct the first column of g, then use gx to construct the rest of g.
Are these the same?
Note that these are two ways to reconstruct g from its gradients. Given arbitrary gx and
gy, these two procedures will not give the same answer, and therefore this pair of gradients
does not correspond to a true surface. Integrability implies that the value of g estimated in
both these ways (or any other way you can think of) is the same. How can we modify the
gradients you calculated above to make gx and gy non-integrable? Why may the gradients
estimated in this way be non-integrable? Note all this down in your write-up.
Q2.4 Extra Credit: Uncalibrated normal estimation (10 pts) Recall the relation
I = L
TB. Here, the L is 3 × N matrix showing the position of light-source where N is
number of light-sources, and B is is the pseudo-normal with shape 3×P, where P is number
of pixels in the image. We know neither L nor B. Therefore, this is a matrix factorization
problem with the constraint that the estimated Lˆ and Bˆ, the rank of ˆI = LˆTB be, and the
estimated ˆI and Lˆ have appropriated dimensions.
It is well known that the best rank-k approximation to a m × n matrix M, where k ≤
min (m, n) is calculated as the following: perform a singular value decomposition SVD M =
, set all singular values except the top k from Σ to 0 to get the matrix Σ, and ˆ
reconstitute Mˆ = UΣˆVT
. Explain in your write-up how this can be used to construct a
factorization of the form detailed above following the required constraints.
Figure 4: Temple images for this assignment
3 Practice: 3D Reconstruction (100 pts, EC 15 pts)
In this part, you will begin by implementing the two different methods seen in class to estimate the fundamental matrix from the corresponding points in two images (subsection 3.2).
Next, given the fundamental matrix and the calibrated intrinsics (which will be provided),
you will compute the essential matrix and use this to compute a 3D metric reconstruction
from 2D correspondences using triangulation (subsection 3.3). Then, you will implement a
method to automatically match points taking advantage of epipolar constraints and make a
3D visualization of the results (subsection 3.4). Finally, you will implement RANSAC and
bundle adjustment to further improve your algorithm (subsection 3.5).
3.2 Fundamental matrix estimation
In this section, you will explore different methods of estimating the fundamental matrix
given a pair of images. In the data/ directory, you will find two images (see Figure 4) from
the Middlebury multiview dataset1
, which is used to evaluate the performance of modern
3D reconstruction algorithms.
The Eight Point Algorithm
The 8-point algorithm (discussed in class, and outlined in Section 10.1 of Forsyth & Ponce)
is arguably the simplest method for estimating the fundamental matrix. For this section,
you can use the provided correspondences you can find in data/some corresp.npz.
Q3.2.1 (10 pts) Finish the function eight-point in submission.py. Make sure you
follow the signature for this portion of the assignment:
F = eightpoint(pts1, pts2, M)
where pts1 and pts2 are N × 2 matrices corresponding to the (x, y) coordinates of the N
points in the first and second image respectively. M is a scale parameter.
Figure 5: displayEpipolarF in helper.py creates a GUI for visualizing epipolar lines
• You should scale the data as was discussed in class, by dividing each coordinate by M
(the maximum of the image’s width and height). After computing F, you will have to
“unscale” the fundamental matrix.
Hint: If xnormalized = Tx, then Funnormalized = T
You must enforce the singularity condition of the F before unscaling.
• You may find it helpful to refine the solution by using local minimization. This probably
won’t fix a completely broken solution, but may make a good solution better by locally
minimizing a geometric cost function.
For this, we have provided a helper function refineF in helper.py taking in F and
two sets of points, which you can call from eightpoint before unscaling F.
• Remember that the x-coordinate of a point in the image is its column entry, and ycoordinate is the row entry. Also note that eight-point is just a figurative name, it just
means that you need at least 8 points; your algorithm should use an over-determined
system (N > 8 points).
• To visualize the correctness of your estimated F, use the supplied function displayEpipolarF
in helper.py, which takes in F, and the two images. This GUI lets you select a point
in one of the images and visualize the corresponding epipolar line in the other image
• Output: Save your matrix F, scale M to the file q3 2 1.npz.
In your write-up: Write your recovered F and include an image of some example
outputs of displayEpipolarF.
The Seven Point Algorithm
Q3.2.2 (Extra Credits, 15 pts) Since the fundamental matrix only has seven degrees
of freedom, it is possible to calculate F using only seven point correspondences. This requires
solving a polynomial equation. In the section, you will implement the seven-point algorithm
(described in class, and outlined in Section 15.6 of Forsyth and Ponce). Manually select 7
points from provided point in data/some corresp.npz, and use these points to recover a
fundamental matrix F. The function should have the signature:
Farray = sevenpoint(pts1, pts2, M)
where pts1 and pts2 are 7 × 2 matrices containing the correspondences and M is the normalizer (use the maximum of the images’ height and width), and Farray is a list array of
length either 1 or 3 containing Fundamental matrix/matrices. Use M to normalize the point
values between [0, 1] and remember to “unnormalize” your computed F afterwards.
• Use displayEpipolarF to visualize F and pick the correct one.
• Output: Save your matrix F, scale M, 2D points pts1 and pts2 to the file q3 2 2.npz.
In your write-up: Write your recovered F and print an output of displayEpipolarF.
Also, include an image of some example output of displayEpipolarF using the seven
• Hints: You can use Numpy’s function roots(). The epipolar lines may not match
exactly due to imperfectly selected correspondences, and the algorithm is sensitive to
small changes in the point correspondences. You may want to try with different sets
3.3 Metric Reconstruction
You will compute the camera matrices and triangulate the 2D points to obtain the 3D scene
structure. To obtain the Euclidean scene structure, first convert the fundamental matrix F
to an essential matrix E. Examine the lecture notes and the textbook to find out how to do
this when the internal camera calibration matrices K1 and K2 are known; these are provided
Q3.3.1 (5 pts) Write a function to compute the essential matrix E given F, K1 and K2
with the signature:
E = essentialMatrix(F, K1, K2)
In your write-up: Write your estimated E using F from the eight-point algorithm.
Given an essential matrix, it is possible to retrieve the projective camera matrices M1 and
M2 from it. Assuming M1 is fixed at [I, 0], M2 can be retrieved up to a scale and four-fold
rotation ambiguity. For details on recovering M2, see section 7.2 in Szeliski. We have provided you with the function camera2 in python/helper.py to recover as the four possible
M2 matrices given E.
Note: The M1 and M2 here are projection matrices of the form: M1 =
Q3.3.2 (10 pts) Using the above, write a function to triangulate a set of 2D coordinates
in the image to a set of 3D points with the signature:
[w, err] = triangulate(C1, pts1, C2, pts2)
where pts1 and pts2 are the N ×2 matrices with the 2D image coordinates and w is an N ×3
matrix with the corresponding 3D points per row. C1 and C2 are the 3 × 4 camera matrices.
Remember that you will need to multiply the given intrinsics matrices with your solution for
the canonical camera matrices to obtain the final camera matrices. Various methods exist
for triangulation – probably the most familiar for you is based on least squares (see Szeliski
Chapter 7 if you want to learn about other methods):
For each point i, we want to solve for 3D coordinates wi =
, such that when they
are projected back to the two images, they are close to the original 2D points. To project
the 3D coordinates back to 2D images, we first write wi
in homogeneous coordinates, and
compute C1w˜i and C2w˜i to obtain the 2D homogeneous coordinates projected to camera 1
and camera 2, respectively.
For each point i, we can write this problem in the following form:
Aiwi = 0 (1)
is a 4×4 matrix, and w˜i
is a 4×1 vector of the 3D coordinates in the homogeneous
form. Then, you can obtain the homogeneous least-squares solution (discussed in class) to
solve for each wi
In your write-up: Write down the expression for the matrix Ai
Once you have implemented triangulation, check the performance by looking at the reprojection error:
||x1i − xc1i
||2 + ||x2i − xc2i
where xc1i = P roj(C1, wi) and xc2i = P roj(C2, wi).
Note: C1 and C2 here are projection matrices of the form: C1 = K1M1 = K1
C2 = K2M2 = K2
Q3.3.3 (10 pts) Write a script findM2.py to obtain the correct M2 from M2s by testing
the four solutions through triangulation. Use the correspondences from data/some corresp.npz.
Output: Save the correct M2, the corresponding C2, and 3D points P to q3 3 3.npz.
3.4 3D Visualization
You will now create a 3D visualization of the temple images. By treating our two images as
a stereo-pair, we can triangulate corresponding points in each image, and render their 3D
Q3.4.1 (15 pts) Implement a function with the signature:
[x2, y2] = epipolarCorrespondence(im1, im2, F, x1, y1)
This function takes in the x and y coordinates of a pixel on im1 and your fundamental
matrix F, and returns the coordinates of the pixel on im2 which correspond to the input
point. The match is obtained by computing the similarity of a small window around the
(x1, y1) coordinates in im1 to various windows around possible matches in the im2 and
returning the closest.
Instead of searching for the matching point at every possible location in im2, we can use
F and simply search over the set of pixels that lie along the epipolar line (recall that the
epipolar line passes through a single point in im2 which corresponds to the point (x1, y1) in
There are various possible ways to compute the window similarity. For this assignment,
simple methods such as the Euclidean or Manhattan distances between the intensity of the
pixels should suffice. See Szeliski Chapter 11, on stereo matching, for a brief overview of
these and other methods.
• Experiments with various window sizes.
• It may help to use a Gaussian weighting of the window, so that the center has greater
influence than the periphery.
• Since the two images only differ by a small amount, it might be beneficial to consider
matches for which the distance from (x1, y1) to (x2, y2) is small.
To help you test your epipolarCorrespondence, we have included a helper function epipolarMatchGUI
in python/helper.py, which takes in two images the fundamental matrix. This GUI allows
you to click on a point in im1, and will use your function to display the corresponding point
in im2. See Figure 6.
Figure 6: epipolarMatchGUI shows the corresponding point found by calling
It’s not necessary for your matcher to get every possible point right, but it should get easy
points (such as those with distinctive corner-like windows). It should also be good enough
to render an intelligible representation in the next question.
Output: Save the matrix F, points pts1 and pts2 which you used to generate the screenshot to the file q3 4 1.npz.
In your write-up: Include a screenshot of epipolarMatchGUI with some detected correspondences.
Figure 7: An example point cloud
Q3.4.2 (10 pts) Included in this homework is a file data/templeCoords.npz which contains 288 hand-selected points from im1 saved in the variables x1 and y1.
Now, we can determine the 3D location of these point correspondences using the triangulate
function. These 3D point locations can then plotted using the Matplotlib or plotly package.
Write a script visualize.py, which loads the necessary files from ../data/ to generate the
3D reconstruction using scatter. An example is shown in Figure 7.
Output: Again, save the matrix F, matrices M1,M2, C1, C2 which you used to generate
the screenshots to the file q3 4 2.npz.
In your write-up: Take a few screenshots of the 3D visualization so that the outline of the
temple is clearly visible, and include them with your homework submission.
3.5 Bundle Adjustment
Bundle Adjustment is commonly used as the last step of every feature-based 3D reconstruction algorithm. Given a set of images depicting a number of 3D points from different
viewpoints, bundle adjustment is the process of simultaneously refining the 3D coordinates
along with the camera parameters. It minimizes reprojection error, which is the squared sum
of distances between image points and predicted points. In this section, you will implement
bundle adjustment algorithm by yourself. Specifically,
• In Q3.5.1, you need to implement a RANSAC algorithm to estimate the fundamental
matrix F and all the inliers.
• In Q3.5.2, you will need to write code to parameterize Rotation matrix R using Rodrigues formula 2
, which will enable the joint optimization process for Bundle Adjustment.
2Please check this pdf for a detailed explanation.
• In Q3.5.3, you will need to first write down the objective function in rodriguesResidual,
and do the bundleAdjustment.
Q3.5.1 (15 pts) In some real world applications, manually determining correspondences
is infeasible and often there will be noisy correspondences. Fortunately, the RANSAC
method seen in class can be applied to the problem of fundamental matrix estimation.
Implement the above algorithm with the signature:
[F, inliers] = ransacF(pts1, pts2, M)
where M is defined in the same way as in subsection 3.2 and inliers is a boolean vector of
size equivalent to the number of points. Here inliers are set to true only for the points that
satisfy the threshold defined for the given fundamental matrix F.
We have provided some noisy coorespondances in some corresp noisy.npz in which around
75% of the points are inliers. Compare the result of RANSAC with the result of the eightpoint algorithm when ran on the noisy correspondences. Briefly, explain the error metrics
you used, how you decided which points were inliers, and any other optimizations you may
Q3.5.2 (15 pts) So far we have independently solved for the camera matrix, Mj and 3D
. In bundle adjustment, we will jointly optimize the reprojection error with
respect to the points wi and the camera matrix wj
||xij − P roj(Cj
where wj = KjMj
For this homework, we are going to only look at optimizing the extrinsic matrix. The
rotation matrix forms the Lie Group SO(3) that doesn’t satisfy the addition operation so
it cannot be directly optimized. Instead, we parameterize the rotation matrix to axis angle
using Rodrigues formula to the Lie Algebra so(3), which is defined in R
. through which the
least squares optimization process can be done to optimize the axis angle. Try to implement
R = rodrigues(r)
as well as the inverse function that converts a rotation matrix R to a Rodrigues vector r
r = invRodrigues(R)
Q3.5.3 (10 pts)
In this section, you need to implement the bundle adjustment algorithm. Using the parameterization you implemented in the last question, write an objective function for the extrinsic
residuals = rodriguesResidual(K1, M1, p1, K2, p2, x)
where x is the flattened concatenation of w, r2, and t2. w are the 3D points; r2 and t2
are the rotation (in the Rodrigues vector form) and translation vectors associated with the
projection matrix M2; p1 and p2 are 2D coordinates of points in image 1 and 2, respectively.
The residuals are the difference between the original image projections and the estimated
projections (the square of 2-norm of this vector corresponds to the error we computed in
residuals = numpy.concatenate([(p1-p1 hat).reshape([-1]),
Use this objective function and Scipy’s nonlinear least squares optimizer leastsq write a
function to optimize for the best extrinsic matrix and 3D points using the inlier correspondences from some corresp noisy.npz and the RANSAC estimate of the extrinsics and 3D
points as an initialization.
[M2, w] = bundleAdjustment(K1, M1, p1, K2, M2 init, p2, p init)
Try to extract the rotation and translation from M2 init, then use invRodrigues you implemented previously to transform the rotation, concatenate it with translation and the 3D
points, then the concatenate vector are variables to be optimized. After obtaining optimized vector, decompose it back to rotation using Rodrigues you implemented previously,
translation and 3D points coordinates.
In your write-up: include an image of the original 3D points and the optimized points as
well as the reprojection error with your initial M2 and w, and with the optimized matrices.
4 Practice: Calibrated Photometric Stereo (35 pt, EC
In this part you will first try to create an lambertian sphere to understand the n-dot-l lighting.
Then, we will try to invert the image formation process you have done for the lambertian
sphere. Seven images of a face lit from different directions are given to us, along with the
ground-truth directions of light sources. The images live in the data/ directory, named as
input n.tif for the n
th image. The source directions are given in the file data/source.npy.
4.1 Lambertian Sphere Rendering
Q4.1 Rendering n-dot-l lighting (10 pts) Consider a uniform fully reflective Lambertian sphere with its center at the origin and a radius of 5 cm. An orthographic camera
located at (0, 0, 10) cm looks towards the negative z-axis with its sensor axes aligned with
the x and y axes. The pixels on the camera are squares 5 µm in size, and the resolution of the
camera is 3000×2500 pixels. Simulate the appearance of the sphere under the n-dot-l model
with directional light sources with incoming lighting directions (1, 1, 1)/
3, (1, −1, 1)/
and (−1, −1, 1)/
3 in the function renderNDotLSphere.
Make sure that the length are in the same unit during implementation. To generate the
Figure 8: Results from calibrated photometric stereo: (a) albedos in the ’gray’ colormap.
(b) normals in the ’rainbow’ colormap and (c) 3D reconstruction of the face.
normal for the sphere surface, use the fact that p
2 + y
2 + z
2 = r, here we can assume x
and y are known. Then calculate the intensity of pixel on the image as I = L
· B where L
is the light direction and B is the pseudo normal.
Note that your rendering isn’t required to be absolutely radiometrically accurate: we only
need to evaluate the n-dot-l model. Include the rendering result in your writeup. (Hint: Recall that for the orthographic projection along the Z axis, consider homogeneous coordinates,
the projection matrix is represented by the form
1 0 0 0
0 1 0 0
0 0 0 1
This mapping takes a point (X, Y, Z, 1)T
to the image point (X, Y, 1)T
, dropping the z
4.2 Estimating pseudonormals and albedos
Q4.2.1 Loading data (5 pts) In the function loadData, read the images into Python
and do the steps as below.
1. Convert the images into the XYZ color space and extract the luminance channel use
provided function lRGB2XYZ in utils.py.Noted that the Y channel is the luminance
2. Vectorize these luminance images and stack them in a 7 × P matrix, where P is the
number of pixels in each image. This is the matrix I, which is given to us by the
3. Load the sources file and covert it to a 3 × 7 matrix L.
Q4.2.2 Estimating pseudonormals (10 pts) Since we have more measurements (7
pixel) than variables (3 per pixel), we will estimate the pseudonormals using least-squares.
Note that there is a linear relation between I and B through L: therefore, we can write
a linear system of the form Ax = y out of the relation I = L
TB and solve it to get B.
Solve this linear system in the function estimatePseudonormalsCalibrated. Estimate perpixel albedos and normals from this matrix in estimateAlbedosNormals. In your write-up,
mention how you construct the matrix A and the vector y.
Note that here matrices you end up creating might be huge and might not fit in your computer’s memory. In that case, to prevent your computer from freezing or crashing completely,
make sure to use the sparse module form scipy. You might also want to use the sparse
linear system solver in the same module.
Q4.2.3 Albedos (5 pts) Note that the albedos are the magnitudes of the pseudonormals
because of the definition of the pseudonormals. Recall the definition of pseudonormals B
defined in : I = L
Calculate the albedos, reshape them into the original size of the images and display the
resulting image in the function displayAlbedosNormals. Include the image in your write-up
and comment on any unusual or unnatural features you may find in the albedo image, and
on why this might be happening. Make sure to display in the gray colormap.
Q4.2.4 Normals (5 pts) The per-pixel, normalized normals can be viewed as an RGB
image. Reshape the estimated normals into an image with 3 channels and display it in the
function displayAlbedosNormals. Note that the components of these normals will have
values in the range [-1, 1]. You will need to rescale them so that they lie in [0, 1] to display
them properly as RGB images. Include this image in the write-up. Do the normals match
your expectation of the curvature of the face? Make sure to display in the rainbow colormap.
4.3 Depth recovering from normals
Q4.3.1 Extra Credit: Normal Integration using Frankot-Chellappa algorithm
(10 points) Read the implementation of integrateFrankot in utils.py, briefly list the
steps (in words & math equations) to show how the integrability is enforced in the algorithm.
Use the algorithm provided to get an estimate of the depth map f(x, y).
Write a function estimateShape to apply the Frankot-Chellappa algorithm to your estimated normals. Once you have the function f(x, y), plot it as a surface in the function
plotSurface and include some significant viewpoints in your write-up.
The 3D projection from mpl toolkits.mplot3d.Axes3D along with the function plot surface
might be of help. Make sure to plot this in the coolwarm colormap.
Q4.3.2 Extra Credit: Undefined quantities (5 pts) The value of F(0, 0) was left
undefined by the algorithm above. Here the F(0, 0) is the 2D Fourier transform values at
zero frequency along x and y axis. Note in your write-up what this tells us in terms of
f(x, y). (Hint: the value of the Fourier transform at zero frequency is the average of the
If your andrew id is bovik, your submission should be the writeup bovik.pdf and a zip file
bovik.zip. Please submit the zip file to Canvas and the pdf file to Gradescope.
The zip file should include the following directory structure:
• submission.py: your implementation of algorithms for section 3 and section 4
• findM2.py: script to compute the correct camera matrix.
• visualize.py: script to visualize the 3d points.
• q3 2 1.npz: file with output of Q3.2.1.
• q3 2 2.npz: file with output of Q3.2.2.
• q3 3 3.npz: file with output of Q3.3.3.
• q3 4 1.npz: file with output of Q3.4.1.
• q3 4 2.npz: file with output of Q3.4.2.
• Answer all questions in the writeup.