MFSeg: Efficient Multi-frame 3D Semantic Segmentation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912364596559872 |
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| author | Huang, Chengjie Czarnecki, Krzysztof |
| author_facet | Huang, Chengjie Czarnecki, Krzysztof |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MFSeg: Efficient Multi-frame 3D Semantic Segmentation Huang, Chengjie Czarnecki, Krzysztof Computer Vision and Pattern Recognition 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. |
| title | MFSeg: Efficient Multi-frame 3D Semantic Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.04408 |