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 indomain 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 semisupervised settings.
}, year = {2024}, journal = {2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {04/2024}, publisher = {IEEE}, address = {Waikoloa, HI, USA}, isbn = {979-8-3503-1892-0}, url = {https://ieeexplore.ieee.org/document/10483852}, doi = {10.1109/WACV57701.2024.00332}, }