Use rotation matrices for OpenCV / PyTorch3D conversions

Summary: Use rotation matrices for OpenCV / PyTorch3D conversions: this avoids hiding issues with conversions to / from axis-angle vectors and ensure new conversion functions have a consistent interface.

Reviewed By: bottler, classner

Differential Revision: D29634099

fbshipit-source-id: 40b28357914eb563fedea60a965dcf69e848ccfa
This commit is contained in:
Patrick Labatut 2021-07-09 10:02:10 -07:00 committed by Facebook GitHub Bot
parent 44d2a9b623
commit fef5bcd8f9
2 changed files with 23 additions and 18 deletions

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@ -13,14 +13,14 @@ from ..transforms import so3_exp_map, so3_log_map
def cameras_from_opencv_projection(
rvec: torch.Tensor,
R: torch.Tensor,
tvec: torch.Tensor,
camera_matrix: torch.Tensor,
image_size: torch.Tensor,
) -> PerspectiveCameras:
"""
Converts a batch of OpenCV-conventioned cameras parametrized with the
axis-angle rotation vectors `rvec`, translation vectors `tvec`, and the camera
rotation matrices `R`, translation vectors `tvec`, and the camera
calibration matrices `camera_matrix` to `PerspectiveCameras` in PyTorch3D
convention.
@ -32,16 +32,20 @@ def cameras_from_opencv_projection(
More specifically, the OpenCV convention projects points to the OpenCV screen
space as follows:
```
x_screen_opencv = camera_matrix @ (exp(rvec) @ x_world + tvec)
x_screen_opencv = camera_matrix @ (R @ x_world + tvec)
```
followed by the homogenization of `x_screen_opencv`.
Note:
The parameters `rvec, tvec, camera_matrix` correspond, e.g., to the inputs
of `cv2.projectPoints`, or to the ouputs of `cv2.calibrateCamera`.
The parameters `R, tvec, camera_matrix` correspond to the outputs of
`cv2.decomposeProjectionMatrix`.
The `rvec` parameter of the `cv2.projectPoints` is an axis-angle vector
that can be converted to the rotation matrix `R` expected here by
calling the `so3_exp_map` function.
Args:
rvec: A batch of axis-angle rotation vectors of shape `(N, 3)`.
R: A batch of rotation matrices of shape `(N, 3, 3)`.
tvec: A batch of translation vectors of shape `(N, 3)`.
camera_matrix: A batch of camera calibration matrices of shape `(N, 3, 3)`.
image_size: A tensor of shape `(N, 2)` containing the sizes of the images
@ -51,7 +55,6 @@ def cameras_from_opencv_projection(
cameras_pytorch3d: A batch of `N` cameras in the PyTorch3D convention.
"""
R = so3_exp_map(rvec)
focal_length = torch.stack([camera_matrix[:, 0, 0], camera_matrix[:, 1, 1]], dim=-1)
principal_point = camera_matrix[:, :2, 2]
@ -84,13 +87,17 @@ def opencv_from_cameras_projection(
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Converts a batch of `PerspectiveCameras` into OpenCV-convention
axis-angle rotation vectors `rvec`, translation vectors `tvec`, and the camera
rotation matrices `R`, translation vectors `tvec`, and the camera
calibration matrices `camera_matrix`. This operation is exactly the inverse
of `cameras_from_opencv_projection`.
Note:
The parameters `rvec, tvec, camera_matrix` correspond, e.g., to the inputs
of `cv2.projectPoints`, or to the ouputs of `cv2.calibrateCamera`.
The outputs `R, tvec, camera_matrix` correspond to the outputs of
`cv2.decomposeProjectionMatrix`.
The `rvec` parameter of the `cv2.projectPoints` is an axis-angle vector
that can be converted from the returned rotation matrix `R` here by
calling the `so3_log_map` function.
Args:
cameras: A batch of `N` cameras in the PyTorch3D convention.
@ -98,7 +105,7 @@ def opencv_from_cameras_projection(
(height, width) attached to each camera.
Returns:
rvec: A batch of axis-angle rotation vectors of shape `(N, 3)`.
R: A batch of rotation matrices of shape `(N, 3, 3)`.
tvec: A batch of translation vectors of shape `(N, 3)`.
camera_matrix: A batch of camera calibration matrices of shape `(N, 3, 3)`.
"""
@ -122,5 +129,4 @@ def opencv_from_cameras_projection(
camera_matrix[:, 2, 2] = 1.0
camera_matrix[:, 0, 0] = focal_length[:, 0]
camera_matrix[:, 1, 1] = focal_length[:, 1]
rvec = so3_log_map(R)
return rvec, tvec, camera_matrix
return R, tvec, camera_matrix

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@ -129,16 +129,15 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
)
camera_matrix[:, :2, 2] = principal_point
rvec = so3_log_map(R)
pts = torch.nn.functional.normalize(torch.randn(4, 1000, 3), dim=-1)
# project the 3D points with the opencv projection function
rvec = so3_log_map(R)
pts_proj_opencv = cv2_project_points(pts, rvec, tvec, camera_matrix)
# make the pytorch3d cameras
cameras_opencv_to_pytorch3d = cameras_from_opencv_projection(
rvec, tvec, camera_matrix, image_size
R, tvec, camera_matrix, image_size
)
# project the 3D points with converted cameras
@ -155,9 +154,9 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
)
# Check the inverse.
rvec_i, tvec_i, camera_matrix_i = opencv_from_cameras_projection(
R_i, tvec_i, camera_matrix_i = opencv_from_cameras_projection(
cameras_opencv_to_pytorch3d, image_size
)
self.assertClose(rvec, rvec_i)
self.assertClose(R, R_i)
self.assertClose(tvec, tvec_i)
self.assertClose(camera_matrix, camera_matrix_i)