  Deep learning-based 3D object detection has achieved unprecedented success
with the advent of large-scale autonomous driving datasets. However, drastic
performance degradation remains a critical challenge for cross-domain
deployment. In addition, existing 3D domain adaptive detection methods often
assume prior access to the target domain annotations, which is rarely feasible
in the real world. To address this challenge, we study a more realistic
setting, unsupervised 3D domain adaptive detection, which only utilizes source
domain annotations. 1) We first comprehensively investigate the major
underlying factors of the domain gap in 3D detection. Our key insight is that
geometric mismatch is the key factor of domain shift. 2) Then, we propose a
novel and unified framework, Multi-Level Consistency Network (MLC-Net), which
employs a teacher-student paradigm to generate adaptive and reliable
pseudo-targets. MLC-Net exploits point-, instance- and neural statistics-level
consistency to facilitate cross-domain transfer. Extensive experiments
demonstrate that MLC-Net outperforms existing state-of-the-art methods
(including those using additional target domain information) on standard
benchmarks. Notably, our approach is detector-agnostic, which achieves
consistent gains on both single- and two-stage 3D detectors.
