def forward(self, theta, P, R_gt, t_gt, C_gt):
if self.gamma > 0.0:
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
t = theta[..., 3:]
losses = torch.cat((
correspondenceLoss(P, C_gt).view(1),
rotationLoss(R, R_gt).view(1), # [0, pi]
translationLoss(t, t_gt).view(1), # [0, inf)
))
loss = losses[0] + self.gamma * (losses[1] + losses[2])
else:
losses = correspondenceLoss(P, C_gt).view(1)
loss = losses[0]
return loss, losses
def forward(self, theta, P, R_gt, t_gt, C_gt, p2d, p3d, num_points_2d, num_points_3d):
# Weight proportionally to the point-set size:
b, m, n = P.size()
weights = torch.min(num_points_2d, num_points_3d).float() / float(min(m, n)) # linear
weights = (2.0 * weights).tanh() # nonlinear
weights = weights.to(device=P.device)
if self.gamma > 0.0:
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
t = theta[..., 3:]
losses = torch.cat((
weightedCorrespondenceLoss(P, C_gt, weights).view(1),
weightedRotationLoss(R, R_gt, weights).view(1), # [0, pi]
weightedTranslationLoss(t, t_gt, weights).view(1), # [0, inf)
))
loss = losses[0] + self.gamma * (losses[1] + losses[2])
else:
losses = weightedCorrespondenceLoss(P, C_gt, weights).view(1)
loss = losses[0]
return loss, losses
def reprojectionErrorsTheta(theta, p2d, p3d, P, eps=1e-7):
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
t = theta[..., 3:]
return reprojectionErrors(R, t, p2d, p3d, P, eps)
def rotationErrorsTheta(theta, R_gt, eps=1e-7):
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
return rotationErrors(R, R_gt, eps)
def numInliersTheta(theta, p2d, p3d, threshold):
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
t = theta[..., 3:]
return numInliers(R, t, p2d, p3d, threshold)
def correspondenceMatricesTheta(theta, p2d, p3d, threshold):
R = geo.angle_axis_to_rotation_matrix(theta[..., :3])
t = theta[..., 3:]
return correspondenceMatrices(R, t, p2d, p3d, threshold)