用知识蒸馏提升运动分割精度与实时性，减少误检。
KDMOS:Knowledge Distillation for Motion Segmentation
- 用鸟瞰图模型作学生，非投影模型作教师，分拆动/静类进行针对性蒸馏。
- 在SemanticKITTI-MOS上达78.8%的IoU，参数量减少7.69%。
- 适合自动驾驶中需高精度实时运动分割的场景。
运动物体分割（MOS）对自动驾驶至关重要，能提升定位、路径规划、地图构建、场景流估计及未来状态预测能力。现有方法虽性能优异，但准确率与实时推理间仍难平衡。为此，我们提出一种基于logits的知识蒸馏框架，旨在提升精度并保持实时效率。具体地，采用基于鸟瞰图（BEV）投影的模型作为学生，非投影模型作为教师。针对移动与非移动类别严重不平衡问题，将二者解耦并应用定制化蒸馏策略，使教师模型更有效学习关键运动特征，显著降低误报与漏报。此外，引入动态上采样，优化网络结构，实现7.69%的参数量减少，缓解过拟合。本方法在SemanticKITTI-MOS隐藏测试集上达到78.8%的显著交并比，并在Apollo数据集上表现良好。代码已开源：https://github.com/SCNU-RISLAB/KDMOS。
原文摘要 · Abstract (English)
Motion Object Segmentation (MOS) is crucial for autonomous driving, as it enhances localization, path planning, map construction, scene flow estimation, and future state prediction. While existing methods achieve strong performance, balancing accuracy and real-time inference remains a challenge. To address this, we propose a logits-based knowledge distillation framework for MOS, aiming to improve accuracy while maintaining real-time efficiency. Specifically, we adopt a Bird's Eye View (BEV) projection-based model as the student and a non-projection model as the teacher. To handle the severe imbalance between moving and non-moving classes, we decouple them and apply tailored distillation strategies, allowing the teacher model to better learn key motion-related features. This approach significantly reduces false positives and false negatives. Additionally, we introduce dynamic upsampling, optimize the network architecture, and achieve a 7.69% reduction in parameter count, mitigating overfitting. Our method achieves a notable IoU of 78.8% on the hidden test set of the SemanticKITTI-MOS dataset and delivers competitive results on the Apollo dataset. The KDMOS implementation is available at https://github.com/SCNU-RISLAB/KDMOS.
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