I want to optimize the light and camera parameters in pytorch3d, but find them to be defined at initial state, so how can I optimize them iteratively ?
for example, the camera is defined as follows:
def SfMPerspectiveCameras( focal_length=1.0, principal_point=((0.0, 0.0),), R=_R, T=_R, device="cpu" ): """ SfMPerspectiveCameras has been DEPRECATED. Use PerspectiveCameras instead. Preserving SfMPerspectiveCameras for backward compatibility. """ warnings.warn( """SfMPerspectiveCameras is deprecated, Use PerspectiveCameras instead. SfMPerspectiveCameras will be removed in future releases.""", PendingDeprecationWarning, ) return PerspectiveCameras( focal_length=focal_length, principal_point=principal_point, R=R, T=T, device=device, )
then the focal_length and principal_point can not be learned by optimization.