LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment
moving objects in point clouds of the current scan using motion information
from previous scans. Despite the promising results achieved by previous MOS
methods, several key issues, such as the weak coupling of temporal and spatial
information, still need further study. In this paper, we propose a novel
LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model,
termed MambaMOS. Firstly, we develop a novel embedding module, the Time Clue
Bootstrapping Embedding (TCBE), to enhance the coupling of temporal and spatial
information in point clouds and alleviate the issue of overlooked temporal
clues. Secondly, we introduce the Motion-aware State Space Model (MSSM) to
endow the model with the capacity to understand the temporal correlations of
the same object across different time steps. Specifically, MSSM emphasizes the
motion states of the same object at different time steps through two distinct
temporal modeling and correlation steps. We utilize an improved state space
model to represent these motion differences, significantly modeling the motion
states. Finally, extensive experiments on the SemanticKITTI-MOS and KITTI-Road
benchmarks demonstrate that the proposed MambaMOS achieves state-of-the-art
performance. The source code of this work will be made publicly available at
this https URL

本研究提出了一种名为 MambaMOS 的基于 LiDAR 的三维运动物体分割方法，利用时间线索引导嵌入模块 (TCBE) 和运动感知状态空间模型 (MSSM) 解决了弱关联时空信息的问题，并通过实验证明其达到了最先进的性能水平。

MambaMOS：基于 LiDAR 的带运动感知状态空间模型的三维移动物体分割

MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware  State Space Model

Recently, numerous approaches have achieved notable success in compressed
video quality enhancement (VQE). However, these methods usually ignore the
utilization of valuable coding priors inherently embedded in compressed videos,
such as motion vectors and residual frames, which carry abundant temporal and
spatial information. To remedy this problem, we propose the Coding
Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial
information from coding priors. The CPGA mainly consists of an inter-frame
temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA)
module. Specifically, the ITA module aggregates temporal information from
consecutive frames and coding priors, while the MNA module globally captures
spatial information guided by residual frames. In addition, to facilitate
research in VQE task, we newly construct the Video Coding Priors (VCP) dataset,
comprising 300 videos with various coding priors extracted from corresponding
bitstreams. It remedies the shortage of previous datasets on the lack of coding
information. Experimental results demonstrate the superiority of our method
compared to existing state-of-the-art methods. The code and dataset will be
released at this https URL

利用编码先验引导的聚合网络（CPGA）通过利用编码先验中的时间和空间信息，成功提升了压缩视频质量增强（VQE）任务的结果，并引入了全新的 Video Coding Priors（VCP）数据集，证明了该方法在 VQE 任务中的卓越性能。