Description
1 Motivation
This goal of this project is to help you understand in a practical way the course material on
camera projection, triangulation, epipolar geometry, and plane warping.
You will be given two views taken at the same time of a person performing a movement in a
motion capture lab.
The person has infrared-reflecting markers attached to his body, and there
are several precisely synchronized and calibrated infrared cameras located around the room
that triangulate the images of these markers to get accurate 3D point measurements.
The two
views you are given are from a pair of visible-light cameras that are also synchronized and
calibrated with respect to the mocap system. As a result, we know the intrinsic and extrinsic
camera calibration parameters of the the camera for each view, and these accurately describe
how the 3D points measured by the mocap system should project into pixel coordinates of each
of the two views.
2 Input Data
You are given the following things:
Two images, im1corrected.jpg and im2corrected.jpg, representing views taken at exactly the
same time by two visible-light cameras in the mocap lab. These images have already been
processed to remove nonlinear radial lens distortion, which is why they are called “corrected”.
Because the lens distortion has been removed, the simple, linear (when expressed in
homogeneous coordinates) pinhole camera model we have studied in class gives a fairly
accurate description of how 3D points in the scene are related to 2D image points and their
viewing rays.
Two matlab files Parameters_V1.mat and Parameters_V2.mat representing the camera
parameters of the two camera views (V1 and V2). Each of these contains a Matlab structure
containing internal/intrinsic and external/extrinsic calibration parameters for each camera.
Part of your job will be figuring out what the fields of the structure mean in regards to the
pinhole camera model parameters we discussed in class lectures. Which are the internal
parameters? Which are the external parameters? Which internal parameters combine to form
the matrix Kmat? Which external parameters combine to form the matrix Pmat?
Hint: the field
“orientation” is a unit quaternion vector describing the camera orientation, which is also
represented by the 3×3 matrix Rmat. What is the location of the camera? Verify that location
of the camera and the rotation Rmat of the camera combine in the expected way (expected as
per one of the slides in our class lectures on camera parameters) to yield the appropriate
entries in Pmat.
A matlab file mocapPoints3D.mat containing 3D point locations of 39 markers on the
performer’s body. These are measured with respect to a “world” coordinate system defined
within the motion capture lab with the origin (0,0,0) located in the middle of the floor, positive
Z-axis pointing vertically upwards, and units measured in millimeters.
3 Tasks to Perform
We want your group to perform the following tasks using the images, mocap points and camera
calibration data:
3.1 Projecting 3D mocap points into 2D pixel locations
Write a function from scratch that takes the 3D mocap points and the camera parameters for
an image and project the 3D points into 2D pixel coordinates for that image.
You will want to
refer to our lecture notes for the transformation chain that maps 3D world coordinates into 2D
pixel coordinates.
For verification, visualize your projected 2D points by plotting the x and y
coordinates of your 2D points onto the image. If your projection function is working correctly,
the points should be close to or overlapping the person’s body, in many cases near the
locations of visible markers attached to the person’s body (other locations will be on markers
that are not visible because they are on the side of the person that is facing away from the
camera).
If the plotted body points are grossly incorrect, such as outlining a shape much larger
or smaller or forming a really weird shape that doesn’t look like it conforms to the arms and
legs of the person in the image), then something is likely wrong in your projection code. Show
that your projection code works correctly for both of the camera views.
3.2 Triangulation to recover 3D mocap points from two views
As a result of the step 3.1 you now have two sets of corresponding 2D pixel locations in the two
camera views. Perform triangulation on each of pair of corresponding 2D points to estimate a
recovered 3D point position.
As per our class lecture on triangulation, this will be done for a
corresponding pair of 2D points by using camera calibration information to convert each into a
viewing ray represented by camera center and unit vector pointing along the ray passing
through the 2D point in the image and out into the 3D scene.
You will then compute the 3D
point location that is closest to both sets of rays (because they might not exactly intersect). Go
back and refer to our lecture on triangulation to see how to do the computation. To verify that
your triangulation code is correct, apply it to all of the 39 mocap points that you projected and
compare how close your set of reconstructed 3D points come to the original set of 3D points
you started with.
You should get reconstructed point locations that are very close to the
original locations. Compute a quantitative error measure such as mean squared error, which is
the average squared distance between original and recovered 3D point locations. It should be
very small.
3.3 Triangulation to make measurements about the scene
After you have verified that your triangulation code is correct, we can start using it to make 3D
measurements in the scene. Specifically, clicking on matching points in the two views by hand,
you can compute by triangulation the 3D scene location that projects to those points. Use this
strategy to verify and to answer the following geometric questions about the scene.
• Measure the 3D locations of at least 3 points on the floor and fit a 3D plane to them.
Verify that your computed floor plane is (roughly) the plane Z=0.
• Measure the 3D locations of at least 3 points on the wall that has white vertical stripes
painted on it and fit a plane. What, approximately, is the equation of the wall plane?
Assuming the floor is Z=0, answer the following questions:
• How tall is the doorway?
• How tall is the person (or more precisely, how high is the top of their head at this
moment)?
• There is a camera mounted on a tall tripod over near the striped wall; what is the 3D
location of the center of that camera (roughly)?
3.4 Compute the Fundamental matrix from known camera calibration parameters
This task might be the hardest in a mathematical sense – compute the 3×3 fundamental matrix
F between the two views using the camera calibration information given. To do this, you will
need to determine from the camera calibration information what the location of camera2 is
with respect to the coordinate system of camera1 (or vice versa) as well as the relative rotation
between them, then combine those to compute the essential matrix E = R S, and finally pre and
post multiply E by the appropriate film-to-pixel K matrices to turn E into a fundamental matrix F
that works in pixel coordinates.
You have all the camera information you need, but it is a little
tricky to get E because although we know how the cameras are related to the same world
coordinate system, we aren’t directly told how the two camera coordinate systems are related
relative to each other – some mathematical derivation is necessary to figure this out. For
example, given the rotation matrices R1 and R2 of the two cameras, the row and columns of
each of them is telling us how to relate camera coord axes to world coords and vice versa.
How, then, would you combine them and/or their inverses/transposes to represent camera 2
axes with respect to the camera 1 coord system? Similarly, how do you compute the position
of camera 2 with respect to the coordinate system of camera 1?
As a sanity check to see if a candidate solution for the F matrix is on the right track, use it map
some points in image 1 into epipolar lines in image 2 to see if they look correct. Also check the
mapping of image 2 points into image 1 epipolar lines.
You are welcome to adapt the section
of code in the eight point algorithm demo (see task 3.5) that draws epipolar lines overlaid on
top of images to do this visualization – you don’t have to figure out how to plot epipolar lines
from scratch.
3.5 Compute the Fundamental matrix using the eight-point algorithm
In contrast, this task may the easiest. Use the eight point algorithm that will be demo’ed in
class and that is available in the matlab sample code section of our course website to compute
a fundamental matrix by selecting matching points in the two views by hand. Recall that for
best results you would like to choose points as spread out across the 3D scene as possible (and
that it would be terrible idea to choose all the points on only a single plane, such as the floor).
The output of the demo code will be a fundamental matrix, and as a byproduct the code plots
epipolar lines in both of the camera views. Show us the matrix and the epipolar plots.
3.6 Quantitative evaluation of your estimated F matrices
Just looking at drawings of the epipolar lines gives us an idea whether an F matrix is roughly
correct, but how to quantitatively measure the accuracy? The symmetric epipolar distance
(SED) is an error measure that evaluates in image coordinates how accurate an estimated
fundamental matrix is based on mean squared geometric distance of points to corresponding
epipolar lines.
“Being immediately physically intuitive, this is the most widely used error
criterion in practice during the outlier removal phase (OpenCV: Open Computer Vision Library,
2009; Snavely et al., 2008; VxL, 2009), during iterative refinement (Faugeras et al., 2001;
Forsyth and Ponce, 2002; Snavely et al., 2008), and in comparative studies to compare the
accuracy of different solutions (Armangu´e and Salvi, 2003; Forsyth and Ponce, 2002; Hartley
and Zisserman, 2004; Torr and Murray, 1997).
Besides being physically intuitive, SED has the
merit of being efficient to compute.” [from Fathy et.al., “Fundamental Matrix Estimation: A
Study of Error Criteria”]. To compute SED, recall that we have a set of 39 accurate 2D point
matches generated in task 3.1. Let the coordinates of one pair of those points be (x1,y1) in
image 1 and (x2,y2) in image 2. For a given fundamental matrix F, compute an epipolar line in
image 2 from (x1,y1) and compute the squared geometric distance of point (x2,y2) from that
line.1
Repeat by mapping point (x2,y2) in image 2 into an epipolar line in image 1 and
measuring squared distance of (x1,y1) to that line. Accumulate these squared distances over all
39 known point matches and at the end compute the mean over all of these squared distances.
That is the SED error to report for the F matrix. Report the SED error for the two matrices you
computed in Task 3.4 and 3.5. Verify that the error for the F matrix computed from known
camera calibration information is much smaller than the error of the F matrix computed using
the eight point algorithm.
That is to be expected. By the way, as a practical use, if you were
using an estimated F matrix to guide the search for point matches in two views, the sqrt of the
SED error gives an idea of how far away from an epipolar line, in pixels, to expect to find a
1 Note: if (a,b,c) are the coefficients of a line and (x,y) is a point then the squared geometric distance of the point
to the line is calculated as (ax+by+c) 2 / (a2
+b2
) .
5
matching point. Thus, this value forms the basis for coming up with a distance threshold to use
for rejecting “outlier” point matches based on the epipolar constraint.
3.6 Generate a similarity-accurate top-down view of the floor plane
Modify our sample code “planewarpdemo” in the matlab sample code section of our course to
generate a higher resolution output than it currently does, for example, by setting the output
(destination) image be comparable in number of rows/cols to the input (source) image.
Also,
note that one deficiency of this code is that the user has to “guess” what the shape of the
chosen rectangle is when specifying the output. Write a new version that does not rely on user
input, and that generates a top-down view of the floor plane that is accurate up to a similarity
transformation (rotation, translation and isotropic scale) with respect to the 2D X-Y world
coordinate system in the floor plane Z=0.
Hint: how can you relate ground plane X-Y
coordinates to 2D image coordinates in the source and in the destination images, given the
known camera parameters of one or both views? Explain how you are generating your topdown view. Also, with regard to the resulting output image, what things look accurate and
what things look weird? Could this kind of view be useful for analyzing anything about the
performance of a person as they move around in the room?
Optional task for extra credit
Going back to the two camera images, crop them to get rid of a lot of the empty lab space in
the images, focusing attention more tightly around the person in the two views. Remember the
parameters of the cropping rectangles used (for example, upper left corner and height/width of
each rectangle), and figure out how to modify the camera intrinsic parameter K matrices to
describe 3D to 2D projection into the pixel coordinates of these cropped images. Also compute
an updated F matrix to map points to lines in these cropped images. Demonstrate that 3D to
2D projection works correctly in your cropped views using your modified camera parameters,
and that your modified F matrix correctly depicts the epipolar geometry between the two
cropped views.
4 What Code Can I Use?
The intent is that you will implement these tasks using general Matlab processing functions
(https://www.mathworks.com/help/matlab/functionlist.html). You can also use and adapt code
from our eight point algorithm and plane warp demo functions available on our Canvas web
site. You MAY NOT use anything from the computer vision toolbox, or any third-party
libraries/packages.
5 What to Hand In?
You will be submitting a single big zip file that contains your code and a narrated video of
roughly 5 minutes in length demonstrating your solutions to each of the given tasks.
CODE:
1) Please organize your code into separate scripts/functions that address each of the tasks,
with names that make it clear which does what, for example task3_1.m, task3_2.m and
so on, so we can easily find what code implements which task. If you have some other
helper functions, please give them descriptive names.
2) Include lots of comments in your functions so that we have a clear understanding of
what it is doing and how it is doing it.
3) Each task script/function should act like a little “demo” in that it produces an output
that convincingly displays that it is coming up with a solution to the given task,
producing not just a text or array output but, whenever possible, a visual display
depicting the output results (for example, by showing images with points and epipolar
lines superimposed on them).
VIDEO REPORT:
1) Include an initial title/credits slide telling who your group members are.
2) Go through task by task in order, explain how you solved it, run your code while we are
watching and show us the output, all while explaining what you are doing and what we
are seeing. Especially if your code is interactive (e.g. clicking points), a video is a good
way to show it running. Give some thought into what you are displaying as output and
why that would convince a viewer that you have come up with a valid solution to the
given task. Also answer any questions that were asked in the task descriptions, and
explain any implementation decisions you made that were clever or unusual. If you
weren’t able to get a working solution to one of the tasks, this is chance to explain
where the difficulty was.
3) Document what each team member did to contribute to the project. It is OK if you
divide up the labor into different tasks (it is expected), and it is OK if not everyone
contributes precisely equal amounts of time/effort on each project. However, if two
people did all the work because the third team member could not be contacted until the
day before the project was due, this is where you get to tell us about it, so the grading
can reflect the inequity.
There are several ways to generate a video with an audio narration track. One of easiest is to
use zoom to record yourself showing slides, programs and/or video results on your computer
while you talk about what you are showing, but feel free to use other recording/editing
software if you like. It is not necessary for all group members to talk – you can nominate one
member to do the narration, or you can take turns talking, it is up to you.
