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About this paper
Kang Zeng, Hao Shi, Jiacheng Lin, Siyu Li, Jintao Cheng, Kaiwei Wang, Zhiyong Li, Kailun Yang
MambaMOS presents a LiDAR-based 3D Moving Object Segmentation framework that employs a Time Clue Bootstrapping Embedding and a Motion-aware State Space Model for robust spatio-temporal feature coupling. The framework achieved state-of-the-art performance on SemanticKITTI-MOS, improving IoU by 2.6% on the hidden test set and demonstrating exceptional accuracy for distant and slow-moving objects.
Key insights
- State Space Models (SSMs) can be effectively adapted for 3D Moving Object Segmentation, offering efficient long-range contextual modeling with linear computational complexity, presenting a viable alternative to transformers.
- Explicitly emphasizing temporal cues and achieving deep-level spatio-temporal feature coupling is critical for accurate motion perception, particularly for challenging sparse and distant objects in LiDAR data.
- Combining multiple space-filling curve serialization patterns enhances the model's ability to capture diverse contextual relationships for dynamic object understanding.
Summary by alphaXiv.
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