  We consider the problem of cross-sensor domain adaptation in the context of
LiDAR-based 3D object detection and propose Stationary Object Aggregation
Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary
objects. In contrast to the current state-of-the-art in-domain practice of
aggregating just a few input scans, SOAP aggregates entire sequences of point
clouds at the input level to reduce the sensor domain gap. Then, by means of
what we call quasi-stationary training and spatial consistency post-processing,
the SOAP model generates accurate pseudo-labels for stationary objects, closing
a minimum of 30.3% domain gap compared to few-frame detectors. Our results also
show that state-of-the-art domain adaptation approaches can achieve even
greater performance in combination with SOAP, in both the unsupervised and
semi-supervised settings.

Computer Vision and Pattern Recognition