  Collaborative 3D object detection exploits information exchange among
multiple agents to enhance accuracy of object detection in presence of sensor
impairments such as occlusion. However, in practice, pose estimation errors due
to imperfect localization would cause spatial message misalignment and
significantly reduce the performance of collaboration. To alleviate adverse
impacts of pose errors, we propose CoAlign, a novel hybrid collaboration
framework that is robust to unknown pose errors. The proposed solution relies
on a novel agent-object pose graph modeling to enhance pose consistency among
collaborating agents. Furthermore, we adopt a multi-scale data fusion strategy
to aggregate intermediate features at multiple spatial resolutions. Comparing
with previous works, which require ground-truth pose for training supervision,
our proposed CoAlign is more practical since it doesn't require any
ground-truth pose supervision in the training and makes no specific assumptions
on pose errors. Extensive evaluation of the proposed method is carried out on
multiple datasets, certifying that CoAlign significantly reduce relative
localization error and achieving the state of art detection performance when
pose errors exist. Code are made available for the use of the research
community at https://github.com/yifanlu0227/CoAlign.
