  In this paper, we study the problem of semi-supervised 3D object detection,
which is of great importance considering the high annotation cost for cluttered
3D indoor scenes. We resort to the robust and principled framework of
selfteaching, which has triggered notable progress for semisupervised learning
recently. While this paradigm is natural for image-level or pixel-level
prediction, adapting it to the detection problem is challenged by the issue of
proposal matching. Prior methods are based upon two-stage pipelines, matching
heuristically selected proposals generated in the first stage and resulting in
spatially sparse training signals. In contrast, we propose the first
semisupervised 3D detection algorithm that works in the singlestage manner and
allows spatially dense training signals. A fundamental issue of this new design
is the quantization error caused by point-to-voxel discretization, which
inevitably leads to misalignment between two transformed views in the voxel
domain. To this end, we derive and implement closed-form rules that compensate
this misalignment onthe-fly. Our results are significant, e.g., promoting
ScanNet<EMAIL_ADDRESS>from 35.2% to 48.5% using 20% annotation. Codes and data will
be publicly available.
