def projective_inverse_warp(target_img, source_img, depth, pose, intrinsics):
    """
    Calculate the reprojected image from the source to the target, based on the given depth, pose and intrinsics

    SFM Learner inverse warp step
        ps ~ K.T(t->s).Dt(pt).K^-1.pt

    Idea is to map the pixel coordinates of the target image to 3d space (Dt(pt).K^-1.pt), then map these onto
    the source image in pixel coordinates (K.T(t->s).{3d coord}), then using the projected coordinates we sample 
    the pixels in the source image (ps) to reconstruct the target image.

    :param target_img: Tensor (batch, height, width, 3)
    :param source_img: Tensor, same shape as target_img
    :param depth: Tensor, (batch, height, width, 1)
    :param pose: (batch, 3, 3)
    :param intrinsics: (batch, 3, 3)
    :return: The source image reprojected to the target
    """
    pass