def _corresponding_cameras_alignment_test_case(
self,
cameras,
R_align_gt,
T_align_gt,
s_align_gt,
estimate_scale,
mode,
add_noise,
):
batch_size = cameras.R.shape[0]
# get target camera centers
R_new = torch.bmm(R_align_gt[None].expand_as(cameras.R), cameras.R)
T_new = (
torch.bmm(T_align_gt[None, None].repeat(batch_size, 1, 1), cameras.R)[:, 0]
+ cameras.T
) * s_align_gt
if add_noise != 0.0:
R_new = torch.bmm(
R_new, so3_exponential_map(torch.randn_like(T_new) * add_noise)
)
T_new += torch.randn_like(T_new) * add_noise
# create new cameras from R_new and T_new
cameras_tgt = cameras.clone()
cameras_tgt.R = R_new
cameras_tgt.T = T_new
# align cameras and cameras_tgt
cameras_aligned = corresponding_cameras_alignment(
cameras, cameras_tgt, estimate_scale=estimate_scale, mode=mode
)
if batch_size <= 2 and mode == "centers":
# underdetermined case - check only the center alignment error
# since the rotation and translation are ambiguous here
self.assertClose(
cameras_aligned.get_camera_center(),
cameras_tgt.get_camera_center(),
atol=max(add_noise * 7.0, 1e-4),
)
else:
def _rmse(a):
return (torch.norm(a, dim=1, p=2) ** 2).mean().sqrt()
if add_noise != 0.0:
# in a noisy case check mean rotation/translation error for
# extrinsic alignment and root mean center error for center alignment
if mode == "centers":
self.assertNormsClose(
cameras_aligned.get_camera_center(),
cameras_tgt.get_camera_center(),
_rmse,
atol=max(add_noise * 10.0, 1e-4),
)
elif mode == "extrinsics":
angle_err = so3_relative_angle(
cameras_aligned.R, cameras_tgt.R
).mean()
self.assertClose(
angle_err, torch.zeros_like(angle_err), atol=add_noise * 10.0
)
self.assertNormsClose(
cameras_aligned.T, cameras_tgt.T, _rmse, atol=add_noise * 7.0
)
else:
raise ValueError(mode)
else:
# compare the rotations and translations of cameras
self.assertClose(cameras_aligned.R, cameras_tgt.R, atol=3e-4)
self.assertClose(cameras_aligned.T, cameras_tgt.T, atol=3e-4)
# compare the centers
self.assertClose(
cameras_aligned.get_camera_center(),
cameras_tgt.get_camera_center(),
atol=3e-4,
)
def compute_corresponding_cameras_alignment():
corresponding_cameras_alignment(
cameras_src, cameras_tgt, estimate_scale=estimate_scale, mode=mode
)
torch.cuda.synchronize()