MFSeg通过特征级融合提升3D语义分割效率，兼顾精度与速度。
MFSeg: Efficient Multi-frame 3D Semantic Segmentation
- 在特征层面融合多帧点云，减少重复计算
- 在nuScenes和Waymo上优于现有方法
- 轻量级MLP解码器避免冗余点上采样
我们提出MFSeg，一种高效的多帧3D语义分割框架。通过在特征层面聚合点云序列，并对特征提取与融合过程进行正则化，MFSeg在保持高精度的同时降低计算开销。此外，采用基于轻量级MLP的点解码器，避免从历史帧中上采样冗余点。在nuScenes和Waymo数据集上的实验表明，MFSeg优于现有方法，验证了其有效性和高效性。
原文摘要 · Abstract (English)
We propose MFSeg, an efficient multi-frame 3D semantic segmentation framework. By aggregating point cloud sequences at the feature level and regularizing the feature extraction and aggregation process, MFSeg reduces computational overhead while maintaining high accuracy. Moreover, by employing a lightweight MLP-based point decoder, our method eliminates the need to upsample redundant points from past frames. Experiments on the nuScenes and Waymo datasets show that MFSeg outperforms existing methods, demonstrating its effectiveness and efficiency.
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