  LiDAR 3D object detection models are inevitably biased towards their training
dataset. The detector clearly exhibits this bias when employed on a target
dataset, particularly towards object sizes. However, object sizes vary heavily
between domains due to, for instance, different labeling policies or
geographical locations. State-of-the-art unsupervised domain adaptation
approaches outsource methods to overcome the object size bias. Mainstream size
adaptation approaches exploit target domain statistics, contradicting the
original unsupervised assumption. Our novel unsupervised anchor calibration
method addresses this limitation. Given a model trained on the source data, we
estimate the optimal target anchors in a completely unsupervised manner. The
main idea stems from an intuitive observation: by varying the anchor sizes for
the target domain, we inevitably introduce noise or even remove valuable object
cues. The latent object representation, perturbed by the anchor size, is
closest to the learned source features only under the optimal target anchors.
We leverage this observation for anchor size optimization. Our experimental
results show that, without any retraining, we achieve competitive results even
compared to state-of-the-art weakly-supervised size adaptation approaches. In
addition, our anchor calibration can be combined with such existing methods,
making them completely unsupervised.
