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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11098 |
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| _version_ | 1866908986522992640 |
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| author | Ren, Zeyi Dong, Jialin Zuo, Wei Wang, Yikun Cheng, Bingyang Zhou, Sheng Niu, Zhisheng |
| author_facet | Ren, Zeyi Dong, Jialin Zuo, Wei Wang, Yikun Cheng, Bingyang Zhou, Sheng Niu, Zhisheng |
| contents | Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes struggle to balance severe pilot overhead with the transmission accuracy required to maintain reconstruction fidelity. To strike a balance between efficiency and reliability, this paper proposes a novel deep learning-based end-to-end (E2E) transceiver design that integrates 3D Gaussian Splatting (3DGS) directly into the training process. By jointly optimizing the communication modules via the combined 3DGS rendering loss, our approach explicitly improves scene recovery quality. Furthermore, this task-driven framework enables the use of a sparse pilot scheme, significantly reducing transmission overhead while maintaining robust image recovery under low-altitude channel conditions. Extensive experiments on real-world aerial image datasets demonstrate that the proposed E2E design significantly outperforms existing baselines, delivering superior transmission performance and accurate 3D scene reconstructions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11098 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction Ren, Zeyi Dong, Jialin Zuo, Wei Wang, Yikun Cheng, Bingyang Zhou, Sheng Niu, Zhisheng Computer Vision and Pattern Recognition Machine Learning Signal Processing Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes struggle to balance severe pilot overhead with the transmission accuracy required to maintain reconstruction fidelity. To strike a balance between efficiency and reliability, this paper proposes a novel deep learning-based end-to-end (E2E) transceiver design that integrates 3D Gaussian Splatting (3DGS) directly into the training process. By jointly optimizing the communication modules via the combined 3DGS rendering loss, our approach explicitly improves scene recovery quality. Furthermore, this task-driven framework enables the use of a sparse pilot scheme, significantly reducing transmission overhead while maintaining robust image recovery under low-altitude channel conditions. Extensive experiments on real-world aerial image datasets demonstrate that the proposed E2E design significantly outperforms existing baselines, delivering superior transmission performance and accurate 3D scene reconstructions. |
| title | Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction |
| topic | Computer Vision and Pattern Recognition Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2604.11098 |