Domain Adaptation for Different Sensor Configurations in 3D Object Detection
This addresses a practical issue for autonomous driving systems by enabling models to adapt to diverse vehicle platforms, though it is incremental as it builds on existing domain adaptation methods.
The paper tackles the problem of performance degradation in 3D object detection when models trained on one LiDAR sensor configuration are applied to another, by proposing Downstream Fine-tuning and Partial Layer Fine-tuning techniques, which consistently outperform naive joint training across different configurations.
Recent advances in autonomous driving have underscored the importance of accurate 3D object detection, with LiDAR playing a central role due to its robustness under diverse visibility conditions. However, different vehicle platforms often deploy distinct sensor configurations, causing performance degradation when models trained on one configuration are applied to another because of shifts in the point cloud distribution. Prior work on multi-dataset training and domain adaptation for 3D object detection has largely addressed environmental domain gaps and density variation within a single LiDAR; in contrast, the domain gap for different sensor configurations remains largely unexplored. In this work, we address domain adaptation across different sensor configurations in 3D object detection. We propose two techniques: Downstream Fine-tuning (dataset-specific fine-tuning after multi-dataset training) and Partial Layer Fine-tuning (updating only a subset of layers to improve cross-configuration generalization). Using paired datasets collected in the same geographic region with multiple sensor configurations, we show that joint training with Downstream Fine-tuning and Partial Layer Fine-tuning consistently outperforms naive joint training for each configuration. Our findings provide a practical and scalable solution for adapting 3D object detection models to the diverse vehicle platforms.