Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915060396326912 |
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| author | Qi, Zipeng Chen, Hao Zhang, Haotian Zou, Zhengxia Shi, Zhenwei |
| author_facet | Qi, Zipeng Chen, Hao Zhang, Haotian Zou, Zhengxia Shi, Zhenwei |
| contents | In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic features of point clouds onto the image plane, simultaneously rendering RGB images and semantic segmentation results. Leveraging the explicit structure of point clouds and a one-time rendering strategy, our approach significantly enhances efficiency during optimization and rendering. Additionally, we employ SAM2 to generate pseudo-labels for boundary regions, which often lack sufficient supervision, and introduce two-level aggregation losses at the 2D feature map and 3D spatial levels to improve the view-consistent and spatial continuity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation Qi, Zipeng Chen, Hao Zhang, Haotian Zou, Zhengxia Shi, Zhenwei Computer Vision and Pattern Recognition In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic features of point clouds onto the image plane, simultaneously rendering RGB images and semantic segmentation results. Leveraging the explicit structure of point clouds and a one-time rendering strategy, our approach significantly enhances efficiency during optimization and rendering. Additionally, we employ SAM2 to generate pseudo-labels for boundary regions, which often lack sufficient supervision, and introduce two-level aggregation losses at the 2D feature map and 3D spatial levels to improve the view-consistent and spatial continuity. |
| title | Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.05969 |