X-MOS: A Heterogeneous Cross-LiDAR Generalization Framework for Moving Object Segmentation

Abstract

Moving object segmentation (MOS) is foundational for autonomous vehicle safety. However, the increasing diversity of LiDAR sensors creates a significant domain shift problem, causing models trained on one sensor to perform poorly when deployed on another. A naive approach of training on combined data from heterogeneous sensors leads to a biased model that favors high-density sensors while failing on sparse, low-resolution sensors. To address this issue, we propose X-MOS, a novel generalization framework based on multi-teacher knowledge distillation. X-MOS generates sensor-specific expert teacher models and employs a sensor-aware knowledge distillation strategy. This strategy uses the sensor type as privileged information to activate the most appropriate teacher at each training step, providing unambiguous learning signals to a single student model. Extensive experiments on the HeLiMOS dataset, which comprises four different LiDAR sensors, demonstrate the effectiveness of our framework. X-MOS mitigates training bias and achieves an overall test mIoU of 0.717, outperforming both naive training and the best individual expert teacher. Notably, it more than doubles the performance on the most challenging low-channel sensor. Furthermore, our model exhibits strong zero-shot generalization to unseen datasets with similar sensor types. This work provides a robust and scalable methodology for achieving cross-sensor generalization, which is foundational for more practical and adaptable perception systems in autonomous driving.