1. Introduction
3D semantic segmentation is a crucial task for applications like autonomous driving and robotics, often relying on multimodal data for robust perception. However, a significant challenge arises when models trained on one data distribution are deployed in environments with differing sensor characteristics or scene properties, leading to a performance drop due to domain shift. This work aims to mitigate this issue by developing a more effective knowledge distillation strategy for multimodal 3D semantic segmentation. Models used in this study include a multimodal Teacher Network (e.g., using Point-Voxel Fusion architectures), and a unimodal Student Network (e.g., PointNet++ or SPVCNN based architectures) operating on point clouds.
2. Related Work
Existing literature has explored various approaches to 3D semantic segmentation, including point-based, voxel-based, and multi-view methods, often benefiting from multimodal inputs like RGB images and LiDAR scans. Knowledge distillation has emerged as a powerful technique to compress large models or transfer knowledge, with recent extensions to multimodal and domain adaptation settings. Prior work in domain adaptation for 3D vision typically focuses on adversarial training or self-training, but few directly address multimodal distillation's specific challenges under domain shift.
3. Methodology
Our proposed methodology introduces a novel multimodal distillation framework that explicitly accounts for domain shift. The core idea involves training a robust multimodal teacher network on source domain data and then distilling its knowledge to a lighter unimodal student network, optimized for target domain performance. Key components include a multimodal feature alignment loss, which encourages the student to learn domain-invariant features, and a cross-modal consistency loss to ensure rich semantic information transfer from the teacher. The distillation process is guided by both response-based and feature-based knowledge transfer mechanisms.
4. Experimental Results
Experiments were conducted on widely recognized outdoor driving datasets, demonstrating the effectiveness of our proposed distillation method. The results consistently show significant improvements in mean Intersection over Union (mIoU) compared to baseline methods and other state-of-the-art domain adaptation techniques. For instance, on the challenging shift from Waymo Open Dataset to nuScenes, our model achieved a notable boost in segmentation accuracy across various classes. The table below summarizes the mIoU performance of different methods on two common target datasets under severe domain shift conditions, illustrating our method's superior generalization capabilities.
5. Discussion
The experimental results clearly indicate that our multimodal distillation framework effectively addresses the performance degradation of 3D semantic segmentation models under domain shift. The success can be attributed to the combined effect of robust multimodal feature learning in the teacher and the principled knowledge transfer mechanisms that enforce domain invariance and cross-modal consistency. Future work could explore adaptive weighting of distillation losses and extending this framework to other 3D perception tasks, such as object detection and tracking, to further enhance real-world applicability and robustness.