119 lines
3.6 KiB
Matlab
119 lines
3.6 KiB
Matlab
function [v1, v2, cam_offsets, t, R ] = createMulti2D2DExperiment( pt_per_cam, cam_number, noise, outlier_fraction )
|
|
|
|
%% generate the camera system
|
|
|
|
avg_cam_distance = 0.5;
|
|
cam_offsets = zeros(3,cam_number);
|
|
%cam_rotations = zeros(3,cam_number*3);
|
|
|
|
if cam_number == 1
|
|
cam_offsets = zeros(3,1);
|
|
%cam_rotations = eye(3);
|
|
else
|
|
for i=1:cam_number
|
|
cam_offsets(:,i) = avg_cam_distance * generateRandomR() * [1.0; 0.0; 0.0];
|
|
%cam_rotations(:,(i-1)*3+1:(i-1)*3+3) = generateRandomR();
|
|
end
|
|
end
|
|
|
|
%% generate random view-points
|
|
|
|
max_parallax = 2.0;
|
|
max_rotation = 0.5;
|
|
|
|
position1 = zeros(3,1);
|
|
rotation1 = eye(3);
|
|
|
|
position2 = max_parallax * 2.0 * (rand(3,1) - repmat(0.5,3,1));
|
|
rotation2 = generateBoundedR(max_rotation);
|
|
|
|
%% Generate random point-clouds
|
|
|
|
minDepth = 4.0;
|
|
maxDepth = 8.0;
|
|
|
|
p = cell([cam_number 1]);
|
|
|
|
for cam=1:cam_number
|
|
normalizedPoints = 2.0*(rand(3,pt_per_cam)-repmat(0.5,3,pt_per_cam));
|
|
norms = sqrt(sum(normalizedPoints.*normalizedPoints));
|
|
directions = normalizedPoints./repmat(norms,3,1);
|
|
p{cam,1} = (maxDepth-minDepth) * normalizedPoints + minDepth * directions;
|
|
end
|
|
|
|
%% Now create the correspondences by looping through the cameras
|
|
|
|
focal_length = 800.0;
|
|
v1 = cell([cam_number 1]);
|
|
v2 = cell([cam_number 1]);
|
|
|
|
for cam=1:cam_number
|
|
|
|
v1{cam,1} = zeros(3,pt_per_cam);
|
|
v2{cam,1} = zeros(3,pt_per_cam);
|
|
|
|
for i=1:pt_per_cam
|
|
|
|
cam_offset = cam_offsets(:,cam);
|
|
%cam_rotation = cam_rotations(:,(cam-1)*3+1:(cam-1)*3+3);
|
|
|
|
body_point1 = rotation1' * (p{cam,1}(:,i)-position1);
|
|
body_point2 = rotation2' * (p{cam,1}(:,i)-position2);
|
|
|
|
% we actually omit the cam rotation here by unrotating the bearing
|
|
% vectors already
|
|
bearingVector1 = body_point1 - cam_offset;
|
|
bearingVector2 = body_point2 - cam_offset;
|
|
bearingVector1_norm = norm(bearingVector1);
|
|
bearingVector2_norm = norm(bearingVector2);
|
|
bearingVector1 = bearingVector1/bearingVector1_norm;
|
|
bearingVector2 = bearingVector2/bearingVector2_norm;
|
|
|
|
% add noise to the bearing vectors here
|
|
bearingVector1_noisy = addNoise(bearingVector1,focal_length,noise);
|
|
bearingVector2_noisy = addNoise(bearingVector2,focal_length,noise);
|
|
|
|
% store the normalized bearing vectors along with the cameras they are
|
|
% being seen (we create correspondences that always originate from the
|
|
% same camera, you should not change this in this experiment!)
|
|
bearingVector1_norm = norm(bearingVector1_noisy);
|
|
bearingVector2_norm = norm(bearingVector2_noisy);
|
|
|
|
v1{cam,1}(:,i) = bearingVector1_noisy./bearingVector1_norm;
|
|
v2{cam,1}(:,i) = bearingVector2_noisy./bearingVector2_norm;
|
|
end
|
|
end
|
|
|
|
%% Add outliers
|
|
outliers_per_cam = floor(outlier_fraction*pt_per_cam);
|
|
|
|
if outliers_per_cam > 0
|
|
for cam=1:cam_number
|
|
|
|
for i=1:outliers_per_cam
|
|
|
|
cam_offset = cam_offsets(:,cam);
|
|
%cam_rotation = cam_rotations(:,(cam-1)*3+1:(cam-1)*3+3);
|
|
|
|
%generate random point
|
|
normalizedPoint = 2.0*(rand(3,1)-repmat(0.5,3,1));
|
|
norm1 = sqrt(sum(normalizedPoint.*normalizedPoint));
|
|
direction = normalizedPoint./norm1;
|
|
point = (maxDepth-minDepth) * normalizedPoint + minDepth * direction;
|
|
|
|
body_point2 = rotation2' * (point-position2);
|
|
|
|
% store the point (no need to add noise)
|
|
bearingVector2 = body_point2 - cam_offset;
|
|
bearingVector2_norm = norm(bearingVector2);
|
|
|
|
v2{cam,1}(:,i) = bearingVector2./bearingVector2_norm;
|
|
end
|
|
|
|
end
|
|
end
|
|
|
|
%% compute relative translation and rotation
|
|
|
|
R = rotation1' * rotation2;
|
|
t = rotation1' * (position2 - position1); |