Issue about the comparison between the predicted pose and the ground truth pose

[LiXinghui-666]

When I compare the optimized camera pose and the real relative pose transformation of the two input images, there is a large deviation, especially when calculating the rotation angle of phi. May I ask what transformation should be done before comparing? I don't know which part is the problem
Below is the part of the code I compared:

optimized relative pose transformation

pose_pred = predicted_poses[-1].copy()
pose_relative = np.linalg.inv(input_pose) @ pose_pred
print('Pose prediction:', np.arctan2(pose_relative[1, 0], pose_relative[0, 0]) * 180 / np.pi)
Output: Pose prediction: 2.1300169042266903

the real relative pose transformation

pose_convert = np.linalg.inv(input_pose_np) @ target_pose_np
phi_convert = np.arctan2(pose_convert[1, 0], pose_convert[0, 0]) * 180 / np.pi
print(f"Phi int: {phi_int}")
print(f"Phi target: {phi_target}")
print(f"Phi convert: {phi_convert}")
Output: Phi int: 155.91559686333247. Phi target: 119.24629196439332. Phi convert: -1.5495584242778404e-05

[yuchen-ji]

When I compare the optimized camera pose and the real relative pose transformation of the two input images, there is a large deviation, especially when calculating the rotation angle of phi. May I ask what transformation should be done before comparing? I don't know which part is the problem Below is the part of the code I compared:

optimized relative pose transformation

pose_pred = predicted_poses[-1].copy() pose_relative = np.linalg.inv(input_pose) @ pose_pred print('Pose prediction:', np.arctan2(pose_relative[1, 0], pose_relative[0, 0]) * 180 / np.pi) Output: Pose prediction: 2.1300169042266903

the real relative pose transformation

pose_convert = np.linalg.inv(input_pose_np) @ target_pose_np phi_convert = np.arctan2(pose_convert[1, 0], pose_convert[0, 0]) * 180 / np.pi print(f"Phi int: {phi_int}") print(f"Phi target: {phi_target}") print(f"Phi convert: {phi_convert}") Output: Phi int: 155.91559686333247. Phi target: 119.24629196439332. Phi convert: -1.5495584242778404e-05

I have the same question, could you give us a little help? @yenchenlin

[KJZhuAutomatic]

@LiXinghui-666 Where you can find the target_pose_np, the example just supply two images of car, even the input_pose is virtual.