export_kitti.py - Camera and lidar do not have the same ego pose

ISSUE

Currently in export_kitti.py the following transformation is incorrect:

            lid_to_ego = transform_matrix(
                cs_record_lid["translation"], Quaternion(cs_record_lid["rotation"]), inverse=False
            )
            ego_to_cam = transform_matrix(
                cs_record_cam["translation"], Quaternion(cs_record_cam["rotation"]), inverse=True
            )
            velo_to_cam = np.dot(ego_to_cam, lid_to_ego)

Unlike nuscenes (which I didn't check, but I believe to be correct), the camera and lidar ego poses for this dataset are not the same. The effect of the code is above is that if you use the RGB camera images with projected labels from lidar the boxes will be randomly off by 10-20 pixels, which is problematic for any sort of 2D learning.

To correct this, two additional transformations are needed to convert to / from world pose for both lidar and camera.

Additionally, if I recall, the render function does not have the same issue as this KITTI converter.

Related PR: https://github.com/lyft/nuscenes-devkit/pull/75

Start in export_kitti.py at the transformation block around line 152 and compare its pose handling with the render function. Trace the lidar and camera transforms through world pose, and review related PR 75. Done means projected lidar labels align with the RGB camera images instead of showing the reported 10–20 pixel offsets.