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function evaluate_poses_keyframe
opt = globals();
% read class names
fid = fopen('classes.txt', 'r');
C = textscan(fid, '%s');
object_names = C{1};
fclose(fid);
% load model points
num_objects = numel(object_names);
models = cell(num_objects, 1);
for i = 1:num_objects
filename = fullfile(opt.root, 'models', object_names{i}, 'points.xyz');
disp(filename);
models{i} = load(filename);
end
% load the keyframe indexes
fid = fopen('keyframe.txt', 'r');
C = textscan(fid, '%s');
keyframes = C{1};
fclose(fid);
% save results
distances_sys = zeros(100000, 5);
distances_non = zeros(100000, 5);
errors_rotation = zeros(100000, 5);
errors_translation = zeros(100000, 5);
results_seq_id = zeros(100000, 1);
results_frame_id = zeros(100000, 1);
results_object_id = zeros(100000, 1);
results_cls_id = zeros(100000, 1);
% for each image
count = 0;
for i = 1:numel(keyframes)
% parse keyframe name
name = keyframes{i};
pos = strfind(name, '/');
seq_id = str2double(name(1:pos-1));
frame_id = str2double(name(pos+1:end));
% load PoseCNN result
filename = sprintf('results_PoseCNN_RSS2018/%06d.mat', i - 1);
result = load(filename);
filename = sprintf('Densefusion_iterative_result/%04d.mat', i - 1);
result_my = load(filename);
filename = sprintf('Densefusion_wo_refine_result/%04d.mat', i - 1);
result_mygt = load(filename);
% load 3D coordinate regression result
filename = sprintf('results_3DCoordinate/%04d.mat', i - 1);
result_3DCoordinate = load(filename);
% load gt poses
filename = fullfile(opt.root, 'data', sprintf('%04d/%06d-meta.mat', seq_id, frame_id));
disp(filename);
gt = load(filename);
% for each gt poses
for j = 1:numel(gt.cls_indexes)
count = count + 1;
cls_index = gt.cls_indexes(j);
RT_gt = gt.poses(:, :, j);
results_seq_id(count) = seq_id;
results_frame_id(count) = frame_id;
results_object_id(count) = j;
results_cls_id(count) = cls_index;
% network result
roi_index = find(result.rois(:, 2) == cls_index);
if isempty(roi_index) == 0
RT = zeros(3, 4);
% pose from network
RT(1:3, 1:3) = quat2rotm(result_my.poses(roi_index, 1:4));
RT(:, 4) = result_my.poses(roi_index, 5:7);
distances_sys(count, 1) = adi(RT, RT_gt, models{cls_index}');
distances_non(count, 1) = add(RT, RT_gt, models{cls_index}');
errors_rotation(count, 1) = re(RT(1:3, 1:3), RT_gt(1:3, 1:3));
errors_translation(count, 1) = te(RT(:, 4), RT_gt(:, 4));
% pose after ICP refinement
RT(1:3, 1:3) = quat2rotm(result.poses_icp(roi_index, 1:4));
RT(:, 4) = result.poses_icp(roi_index, 5:7);
distances_sys(count, 2) = adi(RT, RT_gt, models{cls_index}');
distances_non(count, 2) = add(RT, RT_gt, models{cls_index}');
errors_rotation(count, 2) = re(RT(1:3, 1:3), RT_gt(1:3, 1:3));
errors_translation(count, 2) = te(RT(:, 4), RT_gt(:, 4));
% pose from multiview
RT(1:3, 1:3) = quat2rotm(result_mygt.poses(roi_index, 1:4));
RT(:, 4) = result_mygt.poses(roi_index, 5:7);
distances_sys(count, 3) = adi(RT, RT_gt, models{cls_index}');
distances_non(count, 3) = add(RT, RT_gt, models{cls_index}');
errors_rotation(count, 3) = re(RT(1:3, 1:3), RT_gt(1:3, 1:3));
errors_translation(count, 3) = te(RT(:, 4), RT_gt(:, 4));
%
% % pose from multiview + ICP
% RT(1:3, 1:3) = quat2rotm(result.poses_multiview_icp(roi_index, 1:4));
% RT(:, 4) = result.poses_multiview_icp(roi_index, 5:7);
% distances_sys(count, 4) = adi(RT, RT_gt, models{cls_index}');
% distances_non(count, 4) = add(RT, RT_gt, models{cls_index}');
% errors_rotation(count, 4) = re(RT(1:3, 1:3), RT_gt(1:3, 1:3));
% errors_translation(count, 4) = te(RT(:, 4), RT_gt(:, 4));
else
distances_sys(count, 1:4) = inf;
distances_non(count, 1:4) = inf;
errors_rotation(count, 1:4) = inf;
errors_translation(count, 1:4) = inf;
end
% 3D Coordinate regression result
roi_index = find(result_3DCoordinate.rois(:, 2) == cls_index);
if isempty(roi_index) == 0
RT = zeros(3, 4);
RT(1:3, 1:3) = quat2rotm(result_3DCoordinate.poses(roi_index, 1:4));
RT(:, 4) = result_3DCoordinate.poses(roi_index, 5:7);
distances_sys(count, 5) = adi(RT, RT_gt, models{cls_index}');
distances_non(count, 5) = add(RT, RT_gt, models{cls_index}');
errors_rotation(count, 5) = re(RT(1:3, 1:3), RT_gt(1:3, 1:3));
errors_translation(count, 5) = te(RT(:, 4), RT_gt(:, 4));
else
distances_sys(count, 5) = inf;
distances_non(count, 5) = inf;
errors_rotation(count, 5) = inf;
errors_translation(count, 5) = inf;
end
end
end
distances_sys = distances_sys(1:count, :);
distances_non = distances_non(1:count, :);
errors_rotation = errors_rotation(1:count, :);
errors_translation = errors_translation(1:count, :);
results_seq_id = results_seq_id(1:count);
results_frame_id = results_frame_id(1:count);
results_object_id = results_object_id(1:count, :);
results_cls_id = results_cls_id(1:count, :);
save('results_keyframe.mat', 'distances_sys', 'distances_non', 'errors_rotation', 'errors_translation',...
'results_seq_id', 'results_frame_id', 'results_object_id', 'results_cls_id');
function pts_new = transform_pts_Rt(pts, RT)
% """
% Applies a rigid transformation to 3D points.
%
% :param pts: nx3 ndarray with 3D points.
% :param R: 3x3 rotation matrix.
% :param t: 3x1 translation vector.
% :return: nx3 ndarray with transformed 3D points.
% """
n = size(pts, 2);
pts_new = RT * [pts; ones(1, n)];
function error = add(RT_est, RT_gt, pts)
% """
% Average Distance of Model Points for objects with no indistinguishable views
% - by Hinterstoisser et al. (ACCV 2012).
%
% :param R_est, t_est: Estimated pose (3x3 rot. matrix and 3x1 trans. vector).
% :param R_gt, t_gt: GT pose (3x3 rot. matrix and 3x1 trans. vector).
% :param model: Object model given by a dictionary where item 'pts'
% is nx3 ndarray with 3D model points.
% :return: Error of pose_est w.r.t. pose_gt.
% """
pts_est = transform_pts_Rt(pts, RT_est);
pts_gt = transform_pts_Rt(pts, RT_gt);
diff = pts_est - pts_gt;
error = mean(sqrt(sum(diff.^2, 1)));
function error = adi(RT_est, RT_gt, pts)
% """
% Average Distance of Model Points for objects with indistinguishable views
% - by Hinterstoisser et al. (ACCV 2012).
%
% :param R_est, t_est: Estimated pose (3x3 rot. matrix and 3x1 trans. vector).
% :param R_gt, t_gt: GT pose (3x3 rot. matrix and 3x1 trans. vector).
% :param model: Object model given by a dictionary where item 'pts'
% is nx3 ndarray with 3D model points.
% :return: Error of pose_est w.r.t. pose_gt.
% """
pts_est = transform_pts_Rt(pts, RT_est);
pts_gt = transform_pts_Rt(pts, RT_gt);
% Calculate distances to the nearest neighbors from pts_gt to pts_est
MdlKDT = KDTreeSearcher(pts_est');
[~, D] = knnsearch(MdlKDT, pts_gt');
error = mean(D);
function error = re(R_est, R_gt)
% """
% Rotational Error.
%
% :param R_est: Rotational element of the estimated pose (3x1 vector).
% :param R_gt: Rotational element of the ground truth pose (3x1 vector).
% :return: Error of t_est w.r.t. t_gt.
% """
error_cos = 0.5 * (trace(R_est * inv(R_gt)) - 1.0);
error_cos = min(1.0, max(-1.0, error_cos));
error = acos(error_cos);
error = 180.0 * error / pi;
function error = te(t_est, t_gt)
% """
% Translational Error.
%
% :param t_est: Translation element of the estimated pose (3x1 vector).
% :param t_gt: Translation element of the ground truth pose (3x1 vector).
% :return: Error of t_est w.r.t. t_gt.
% """
error = norm(t_gt - t_est); |