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.