Description
Part 1: Camera Projection Matrix Estimation
Learning Objective: (1) Understanding the the camera projection matrix and (2) estimating it using
fiducial objects for camera projection matrix estimation and pose estimation.
Introduction
In this first part you will perform pose estimation in an image taken by an uncalibrated camera. As
we saw in class, pose estimation is incredibly useful; it is used in VR, AR, controller tracking,
autonomous driving, and even satellite docking. Recall that for a pinhole camera model, the camera
matrix is a projective mapping from world (3D) to pixel (2D) coordinates defined up to a
scale.
Above is an arbitrary scale factor. The camera matrix can also be decomposed into intrinsic
parameters and extrinsic parameters .
Let’s look more carefully into what each of the individual parts of the decomposed matrix mean. The
homogenous vector coordinates of indicate the position of a point in 3D space in
the world coordinate system. The matrix represents a translation and the matrix
represents a rotation. When combined they convert points from the world to the camera coordinate
system. An intuitive way to understand this is to think about how aligning the axes of the world
coordinate system to the ones of the camera coordinate system can be done with a rotation and a
translation.
The distinction between camera coordinate and world coordinate systems is a rotation and a
translation.
In this part of the project you will learn how to estimate the projection matrix using objective
function minimization, how you can decompose the camera matrix, and what knowing these lets you
do.
Part 1.1: Implement Camera Projection
In projection_matrix.py you will implement camera projection in the projection(P,
points_3d) from homogenous world coordinates to non-homogenous image
coordinates .
Given the projection matrix , the equation that accomplish this are:
Part 1.2: Implement Objective Function
A camera projection matrix maps points from 3D into 2D. How can we use this to estimate its
parameters? Assume that we have known 2D-3D correspondences for a set of points, that is, for
points with index we have both access to the respective 3D coordinates and 2D
coordinates . Let be an estimation for the camera projection matrix. We can determine how
accurate the estimation is by measuring the reprojection error
between the 3D points projected into 2D and the known 2D points , both in nonhomogeneous coordinates. Therefore we can estimate the projection matrix itself by minimizing the
reprojection error with respect to the projection matrix
In this part, in projection_matrix.py you will implement the objective function
objective_function() that will be passed to scipy.optimize.least_squares for minimization
with the Levenberg-Marquardt algorithm.
Part 1.3: Estimating the Projection Matrix Given Point
Correspondences
Optimizing the reprojection loss using Levenberg-Marquardt requires a good initial estimate for .
This can be done by having good initial estimates for and and which you can multiply to then
generate your estimated . In this part, to make sure that you have the least squares optimization
working properly we will provide you with an initial estimate. In the function you will have to
implement in this part, estimate_projection_matrix() , you will have to pass the initial guess to
scipy.optimize.least_squares and get the appropriate output.
Note: because has only 11 degrees of freedom, we fix .
Part 1.4: Decomposing the Projection Matrix
Recall that
.
Rewriting this gives us:
Where is the first 3 columns of . An operation known as RQ decomposition which will
decompose into an upper triangular matrix and an orthonormal matrix such that ,
where the upper triangular matrix will correspond to and the orthonormal matrix to . In this
part you will implement decompose_camera_matrix(P) where you will need to get the appropriate
matrix elements of to perform the RQ decomposition, and make the appropriate function call to
scipy.linalg.rq() .
Part 1.5: Calculating the Camera Center
In this part in projection_matrix.py you will implement calculate_camera_center(P, K, R)
that takes as input the
projection , intrinsic and rotation matrix and outputs the camera position in world
coordinates.
Part 1.6: Taking Your Own Images and Estimating the
Projection Matrix + Camera Pose
In part 1.3 you were given a set of known points in world coordinates. In this part of the assignment
you will learn how to use a fiducial—an object of known size that can be used as a reference. Any
object for which you have measured the size given some unit (in this project you should use
centimeters).
Example of how one cuboid of known dimensions can be used as a fiducial to create multiple world
coordinate systems
The figure above illustrates how a cuboid of known dimension can be used to create a world
coordinate system and a set of points with known 3D location. Choose an object that you will use as
a fiducial (we recommend using a thick textbook) and measure it. Using a camera capture two
images of the object (you will estimate the camera parameters for both images) keeping in mind the
considerations discussed in class for part 2 and fundamental matrix estimation if you want to reuse
these images. When taking the images, try to estimate the pose of the camera lens of your phone in
the world coordinate system.
Now that you have the dimension of your object (3D points), you can use the Jupyter notebook to
find the image coordinates of the 3D points create your own 2D-3D correspondences for each image.
For each of your 2 images, make initial estimates for and if your estimate is good, using your code
from the previous part you should be able to estimate both the the projection matrix and the camera
pose. Use the code available in the Jupyter notebook to visualize your findings.
Report
Put these answers in your report!
What would happen to the projected points if you increased/decreased the coordinate, or the
other coordinates of the camera center ? Write down a description of your expectations in the
appropriate part of your writeup submission.
Perform this shift for each of the camera coordinates and then recompose the projection matrix
and visualize the result in your Jupyter notebook. Was the visualized result what you expected?
Part 2: Fundamental Matrix Estimation
Diagram of epipolar lines and epipoles. The fundamental matrix maps points from one image to an
epipolar line on the other.
Learning Objective: (1) Understanding the fundamental matrix and (2) estimating it using selfcaptured images to estimate your own fundamental matrix.
In this part, given a set of corresponding 2D points, we will estimate the fundamental matrix. Now
that we know how to project a point from a 3D coordinate to a 2D coordinate, next we’ll look at how
to map corresponding 2D points from two images of the same scene. You can think of the
fundamental matrix as something that maps points from one view into a line in the other view. We
get a line because a point in one image is only a projection to 2D, which means we can’t actually
know the “depth” of that point. As such, from the viewpoint of the other camera, we can see the
entire “line” that our first point could exist on.
The fundamental matrix constraint between two points and in the left and right views,
respectively, is given by the following equation:
Above and are homogenous coordinates in the two views, and is the fundamental
matrix. We can write out the equation above as:
Since takes points in one image and maps them to a line in another image, the constraint says that
we want the point to be on the line represented by . If the point is exactly on the line, then the
constraint and the line-to-point distance between them is also zero.
Hence to estimate we can minimize the line-to-point distance between the point and the line
, for all matching points . This makes estimating the fundamental matrix a least squares
problem.
Part 2.1: Distance Formula
The line-to-point distance formula is defined by the following equation:
where is a line and is the homogenous coordinate for the point. You will
need to implement this in point_line_distance() in fundamental_matrix.py .
Part 2.2: Symmetric line-to-point error
Given a set of matching points we can set up the following symmetric line-to-point error
function for every match .
where is the distance formula between a line and a point, is one of the homogeneous points,
is the fundamental matrix, and is the other homogeneous point. In the first term we use , which
maps from the second to the first view, and in second term we use , which maps from the first to
the second view. SciPy handles the squaring and summing for you so you just need to implement
point_line_distance() .
By applying this symmetric line-to-point error calculation to every pair of points, we get this equation
for the objective function to minimize, as a function of :
This is the equation you will be implementing in signed_point_line_errors() which is in the file
fundamental_matrix.py .
Part 2.3: Least Squares Optimization
Since this is a least squares optimization problem, you will also be making the call to SciPy’s least
squares optimizer. The documentation for this is available here.
You’ll need to give as input: the objective function, your initial estimate of the fundamental matrix,
optimization algorithm, method for computing the Jacobian, and the point pairs. There are more
details on this in the code.
Part 2.4: Try it Yourself
Similar to Part 1, you’ll have to take two images of the same scene and estimate the fundamental
matrix between the two images. Recall that these two images must be from different positions, and
you cannot simply just rotate the camera or zoom the image. You’ll have to save the images in the
same project folder and use the Jupyter notebook to run your fundamental matrix estimator on your
images.
Report
Put these answers in your report!
Why is it that when you take your own images, you can’t just rotate the camera or zoom the
image for your two images of the same scene?
Why is it that points in one image are projected by the fundamental matrix onto epipolar lines in
the other image?
What happens to the epipoles and epipolar lines when you take two images where the camera
centers are within the images? Why?
What does it mean when your epipolar lines are all horizontal across the two images?
Why is the fundamental matrix defined up to a scale?
Why is the fundamental matrix rank 2?
Part 3: RANSAC
Learning objective: (1) Understand the parameters of the RANSAC algorithm. (2) Apply RANSAC to
find the fundamental matrix for a pair of images given imperfect point correspondences (ie outliers).
Now you have a function which can calculate the fundamental matrix from matching pairs of
points in two different images. However, having to manually extract the matching points is
undesirable. Later in the course, you will learn to to automate the process of identifying matching
points in two images. For now we will assume that someone has already run code to find the
matches for you; however, the automated matching is not perfect, and we will use RANSAC to find a
set of true matches (inliers) to calculate . Fortunately, to calculate the fundamental matrix , we
need only 9 matching points, but we want to automate the process of finding them. Below is a highlevel description of how we will implement this section. See the Jupyter notebook and code
documentation for implementation details.
Part 3.1: RANSAC Iterations
We will use a method called RANdom SAmple Consensus (RANSAC) to search through the points
returned by SIFT and find true matches to use for calculating the fundamental matrix . Please
review the lecture slides on RANSAC to refresh your memory on how this algorithm works.
Additionally, you can find a simple explanation of RANSAC at
https://www.mathworks.com/discovery/ransac.html. See section 6.1.4 in the textbook for a more
thorough explanation of how RANSAC works.
You may wonder how many iterations of this algorithm we need to run in order to acheive success. It
turns out RANSAC actually provides probabilistic guarentees of finding the correct model for our
dataset given the number of iterations we run, the size of our sample, and the proportion of true
matches in our dataset. You will right a function to determine how many iterations of RANSAC to run
in order to find the fundamental matrix with a high probability of success. The jupyter notebook will
guide you through this derivation.
Part 3.2: RANSAC Implementation
Next we will implement the RANSAC algorithm. Remember the steps from the link above:
1. Randomly selecting a subset (k=9) of the data set
2. Fitting a model to the selected subset
3. Determining the number of outliers
4. Repeating steps 1-3 for a prescribed number of iterations
For the application of finding true point pair matches and using them to calculate the fundamental
matrix, our subset of the data will be the minimum number of point pairs needed to calculate the
fundamental matrix.
The model we are fitting is the fundamental matrix.
Outliers will be found by using the point_line_distance() error function from part 2 and
thresholding with a certain margin of error.
Part 3.3: RANSAC Visualization
Finally, we will demonstrate running RANSAC on a real image pair, and plot the epipolar lines from
the resulting fundamental matrix. You do not need to write any new code for this section, but use it
as an opportunity in addition to the unit tests to check your results and better understand epipolar
geometry and RANSAC.
Report
Put these answers in your report!
What is the minimum number of RANSAC iterations we would we need to find the fundamental
matrix with 99.9% certainty from a set of proposed matches that have a 90% point
correspondence accuracy? Keep in mind that we need at least 9 point correspondences for our
optimization to find the fundamental matrix in part 2
One might imagine that if we had more than 9 point correspondences, it would be better to use
more of them to solve for the fundamental matrix. Investigate this by finding the number of
RANSAC iterations you would need to run for the above situation with 18 points.
If our dataset had a lower point correspondence accuracy, say 70%, what is the minimum
number of iterations needed to find the fundamental matrix with 99.9% certainty?
Part 4: Recovering relative camera pose from epipolar
geometry
Note: This section is compulsory for graduate students. Undergraduate students are welcome to
attempt this section for extra credits.
Once we have the fundamental matrix, we will try and recover the relative rotation and translation
between two camera poses.
4.1 Recovering essential matrix from the fundamental matrix
Let’s work out some of the math and introduce the essential matrix.
Recall the epipolar constraint . If we assume that both these camera share the same
calibration matrix , we can write the projection equation for and . Let and be the
extrinsic matrix for the two cameras. Without loss of generality, we can fix the world coordinate
system such that is . We can then represent as where and are the relative
rotation and translation between the two cameras.
where and are called normalized image coordinates (image coordinates with the effect of
camera intrinsics removed), and are called normalized camera matrices.
We define the essential matrix as the fundamental matrix corresponding to the a pair of
normalized cameras. We can write the epipolar constraint as :
You can now write the essential matrix in terms of . Derive the equation and write the code in
recover_E_from_F() function in recover_rot_translation.py .
Please note that this writeup is not thorough and we expect you to refer to the lecture slides. Section
9.6 from the book Multi-view geometry by Hartley or Zisserman (or any other material of your
preference) is also a good reference
4.2 Recovering relative rotation and translation
We have now computed the essential matrix. Please follow the derivation in the lecture slides for the
essential matrix equation in terms of and : .
We will follow section 9.6.2 from the book multi-view geometry for this part. Note that the proof in
this writeup is not comprehensive and we expect you to go through the detailed proof.
Let us create two constant matrices to help with the notation:
Note that W is an orthogonal matrix, and Z is a skew-symmetric matrix. We can use SVD to write
.
Our goal is to decompose as . where is a skew symmetric matrix and is a rotation
matrix.
As and have the same left null space, we can write . As rotation matrices are
orthonormal, we can write in terms of another rotation matrix , using and which are
orthonormal: .
Plugging in the values of and in the SVD decomposition of , we get two possible values of :
or .
We have to derive the value of from . As we can only derive upto scale, we will restrict the l2-
norm of to be 1. As is a proxy for , we can use the relationship to calculate as the the
third column of .
Hence we have two possible values of rotation, and two possible values of normalized translation
(positive and negative sign).
Complete the function recover_rot_translation_from_E in recover_rot_translation.py .
There is no deliverable in report for this section.
Testing
We have provided a set of tests for you to evaluate your implementation. We have included tests
inside proj2.ipynb so you can check your progress as you implement each section. When you’re
done with the entire project, you can call additional tests by running pytest inside the
“./proj2_unit_tests” directory of the project. Your grade on the coding portion of the project will be
further evaluated with a set of tests not provided to you.
Bells & Whistles (Extra Points)
This assignment includes 10 points of extra for implementing SVD to decompose the essential matrix
as described above. All grad students are required to complete the extra credit.
Writeup
For this project (and all other projects), you must do a project report using the template slides
provided to you. Do not change the order of the slides or remove any slides, as this will affect the
grading process on Gradescope and you will be deducted points. In the report you will describe your
algorithm and any decisions you made to write your algorithm a particular way. Then you will show
and discuss the results of your algorithm. The template slides provide guidance for what you should
include in your report. A good writeup doesn’t just show results–it tries to draw some conclusions
from the experiments. You must convert the slide deck into a PDF for your submission.
If you choose to do anything extra, add slides after the slides given in the template deck to describe
your implementation, results, and analysis. Adding slides in between the report template will cause
issues with Gradescope, and you will be deducted points. You will not receive full credit for your extra
credit implementations if they are not described adequately in your writeup.
Rubric
+66 (+10) pts: Code
22 pts: Part 1
22 pts: Part 2
22 pts: Part 3
10 pts: Part 4
+34 pts: PDF report
10 pts: Part 1
10 pts: Part 2
10 pts: Part 3
4 pts: Conclusions
-5*n pts: Lose 5 points for every time you do not follow the instructions for the hand-in format.
Submission Format
This is very important as you will lose 5 points for every time you do not follow the instructions. You
will have two submission files for this project:
1. .zip via Gradescope containing:
proj2_code/ – directory containing all your code for this assignment
2. _proj2.pdf via Gradescope – your report
Do not install any additional packages inside the conda environment. The TAs will use the same
environment as defined in the config files we provide you, so anything that’s not in there by default
will probably cause your code to break during grading. Do not use absolute paths in your code or
your code will break. Use relative paths like the starter code already does. Failure to follow any of
these instructions will lead to point deductions. Create the zip file using python zip_submission.py
–gt_username (it will zip up the appropriate directories/files for you!) and
hand it through Canvas. Remember to submit your report as a PDF to Gradescope as well.
Credit
Assignment developed by Jacob Knaup, Julia Chen, Stefan Stojanov, Frank Dellaert, and James Hays
based on a similar project by Aaron Bobick; updated by Ayush Baid, Jacob Knaup, and Judy Hoffman.
