SOAP: Cross-Sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-Labelling
Abstract
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.
Cite
Text
Huang et al. "SOAP: Cross-Sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-Labelling." Winter Conference on Applications of Computer Vision, 2024.Markdown
[Huang et al. "SOAP: Cross-Sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-Labelling." Winter Conference on Applications of Computer Vision, 2024.](https://mlanthology.org/wacv/2024/huang2024wacv-soap/)BibTeX
@inproceedings{huang2024wacv-soap,
title = {{SOAP: Cross-Sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-Labelling}},
author = {Huang, Chengjie and Abdelzad, Vahdat and Sedwards, Sean and Czarnecki, Krzysztof},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2024},
pages = {3352-3361},
url = {https://mlanthology.org/wacv/2024/huang2024wacv-soap/}
}