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arxiv: 2604.11098 · v2 · submitted 2026-04-13 · 💻 cs.CV · cs.LG· eess.SP

Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction

Zeyi Ren , Jialin Dong , Wei Zuo , Yikun Wang , Bingyang Cheng , Sheng Zhou , Zhisheng Niu This is my paper

Pith reviewed 2026-05-10 15:35 UTC · model grok-4.3

classification 💻 cs.CV cs.LGeess.SP
keywords aerial image transmission3D scene reconstructionend-to-end transceiver3D Gaussian Splattingsparse pilotslow-altitude networksdeep learningwireless communication
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The pith

Integrating 3D Gaussian Splatting rendering loss into end-to-end transceiver training allows sparse pilots while preserving accurate large-scale 3D scene reconstruction from aerial images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a deep learning transceiver that transmits aerial images for 3D scene reconstruction in low-altitude networks. It folds the 3DGS rendering loss directly into the joint training of the communication modules so that transmission decisions improve the final reconstructed scene rather than optimizing for image fidelity alone. This task-driven setup supports a sparse pilot pattern that cuts transmission overhead yet keeps reconstruction quality high under realistic low-altitude channel conditions. Experiments on real-world aerial datasets show the design outperforms conventional separate source-channel schemes in both transmission efficiency and reconstruction accuracy.

Core claim

By embedding the 3D Gaussian Splatting rendering loss into the end-to-end optimization of the transceiver, the system simultaneously improves scene recovery quality and permits a sparse pilot scheme that reduces transmission overhead while maintaining robust image recovery under low-altitude channel conditions.

What carries the argument

End-to-end transceiver whose communication modules are jointly optimized with the 3D Gaussian Splatting rendering loss as the training objective.

If this is right

  • Scene reconstruction quality improves because the transceiver is trained to minimize the final 3DGS rendering error rather than pixel-wise image error.
  • Pilot overhead drops substantially through the learned sparse pilot scheme while image recovery remains robust under low-altitude fading.
  • The same framework can be applied to other 3D reconstruction pipelines by swapping the rendering loss used during transceiver training.
  • Transmission latency and bandwidth usage decrease, enabling more frequent image uploads from aerial platforms without sacrificing reconstruction fidelity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested on video streams to support dynamic scene updates rather than static reconstructions.
  • If channel statistics change slowly, periodic fine-tuning of the transceiver on recent flights might further reduce overhead.
  • Hardware implementations would need to verify whether the learned sparse pilots remain effective when the receiver has only imperfect channel state information.

Load-bearing premise

The 3DGS rendering loss remains a faithful and stable training signal for the transceiver when real low-altitude channels differ from the training distribution and the learned sparse pilot pattern generalizes beyond the tested aerial datasets.

What would settle it

Train the transceiver on one real-world aerial dataset, then evaluate 3D reconstruction metrics on a second dataset collected under measurably different low-altitude channel statistics; if reconstruction quality falls below that of a conventional baseline transceiver, the joint-optimization claim does not hold.

Figures

Figures reproduced from arXiv: 2604.11098 by Bingyang Cheng, Jialin Dong, Sheng Zhou, Wei Zuo, Yikun Wang, Zeyi Ren, Zhisheng Niu.

Figure 1
Figure 1. Figure 1: Illustration of the low-altitude intelligent network imagery scenario. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the proposed task-oriented end-to-end transceiver. The transmitter utilizes a Transformer-based Moduformer for resilient constellation [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: BLER comparison of various transmission schemes versus SNR. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effective throughput comparison across varying SNRs. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative visualization of the reconstructed 3D scene (HAV). [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative visualization with and without the task-oriented loss. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper proposes a deep learning-based end-to-end transceiver for aerial image transmission in low-altitude intelligent networks (LAIN). It integrates 3D Gaussian Splatting (3DGS) rendering loss directly into transceiver training to jointly optimize communication modules, enabling both higher-fidelity 3D scene reconstruction and a sparse pilot scheme that reduces overhead while maintaining robustness under low-altitude channel conditions. Experiments on real-world aerial datasets reportedly show outperformance over baselines in transmission and reconstruction quality.

Significance. If the joint optimization via the 3DGS loss holds under the reported conditions, the work meaningfully advances task-driven semantic communications by linking transceiver design to downstream 3D reconstruction objectives. This could reduce pilot overhead in UAV-based large-scale mapping without sacrificing fidelity, with practical value for LAIN applications. The empirical focus on real aerial datasets and ablation of the sparse-pilot component are strengths that support potential impact in the intersection of CV and wireless systems.

major comments (2)
  1. [§3] §3 (Proposed Method), loss formulation: the claim that the combined 3DGS rendering loss enables stable end-to-end optimization for the sparse pilot scheme requires explicit specification of the weighting hyperparameter between the rendering loss and any communication-specific terms (e.g., reconstruction or channel loss); without this, it is unclear whether the reported robustness is due to the task-driven signal or careful tuning.
  2. [§4.2] §4.2 (Experiments, channel conditions): the ablation tables demonstrate gains from the sparse pilot scheme, but the tested low-altitude channel variations (specific Doppler, multipath, or SNR ranges) are not enumerated with sufficient granularity to confirm generalization beyond the training datasets, which directly bears on the weakest assumption about stability under real variations.
minor comments (3)
  1. [Figures] Figure 3 (or equivalent comparison figure): axis labels and legend entries should explicitly state the metrics (e.g., PSNR, SSIM, BER) and baseline names to improve readability of the outperformance claims.
  2. [§2] Notation in §2 (System Model): the definitions of the transceiver modules (encoder/decoder) and pilot insertion could use a consistent symbol table or diagram to avoid ambiguity when describing the E2E differentiability.
  3. [Abstract] The abstract states 'significantly outperforms' without quantifying the gains; a brief summary of key metrics (e.g., dB improvement) would strengthen the opening claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and constructive feedback. The two major comments can be fully addressed through clarifications and additions in the revised manuscript, which we believe will further strengthen the presentation of the joint optimization and experimental robustness.

read point-by-point responses

  1. Referee: [§3] §3 (Proposed Method), loss formulation: the claim that the combined 3DGS rendering loss enables stable end-to-end optimization for the sparse pilot scheme requires explicit specification of the weighting hyperparameter between the rendering loss and any communication-specific terms (e.g., reconstruction or channel loss); without this, it is unclear whether the reported robustness is due to the task-driven signal or careful tuning.

    Authors: We agree that explicit specification improves clarity. The loss is formulated as L_total = L_3DGS + λ L_comm, where L_comm combines MSE reconstruction and channel estimation terms. The hyperparameter λ was set to 0.7 after grid search over {0.1, 0.5, 0.7, 1.0} on a validation split, selected because it yielded stable convergence while allowing the 3DGS task loss to meaningfully guide the transceiver. We will add this exact formulation, the chosen value, and a one-sentence justification to §3.3 in the revision. revision: yes

  2. Referee: [§4.2] §4.2 (Experiments, channel conditions): the ablation tables demonstrate gains from the sparse pilot scheme, but the tested low-altitude channel variations (specific Doppler, multipath, or SNR ranges) are not enumerated with sufficient granularity to confirm generalization beyond the training datasets, which directly bears on the weakest assumption about stability under real variations.

    Authors: We acknowledge that greater granularity on the channel parameters will help readers assess generalization. The experiments used the 3GPP low-altitude UAV channel model with Doppler shifts uniformly sampled from 0–250 Hz, multipath delay spreads of 1–8 μs, and SNR levels from 8 dB to 28 dB. We will insert a new table in §4.2 that explicitly lists these ranges together with the number of Monte-Carlo realizations per setting, and we will add a short paragraph confirming that the sparse-pilot gains remain consistent across the full range. No new experiments are required; the data already exist in our logs. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents an empirical end-to-end deep learning transceiver design that incorporates 3D Gaussian Splatting rendering loss as the training objective for joint optimization of communication modules. All performance claims are supported by experiments on real-world aerial datasets measured against external baselines, with no derivation step that reduces a prediction, uniqueness result, or first-principles claim to a self-definition, fitted input, or self-citation chain. The sparse pilot scheme follows directly from the differentiability of the combined loss, which is an independent property of the model architecture rather than a constructed equivalence.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard deep-learning assumptions (differentiable rendering, stable gradient flow through the communication channel model) plus the domain assumption that 3DGS loss is a suitable surrogate for reconstruction fidelity. No new physical entities are postulated.

free parameters (1)
  • neural network weights and hyperparameters
    All transceiver and 3DGS parameters are learned from data; their specific values are not reported in the abstract.
axioms (2)
  • domain assumption The wireless channel can be modeled sufficiently accurately for end-to-end gradient-based training.
    Implicit in any E2E communication design; required for the joint optimization to be valid.
  • domain assumption 3D Gaussian Splatting rendering loss correlates with final scene reconstruction quality under the target channel conditions.
    Central modeling choice that allows the task-driven training.

pith-pipeline@v0.9.0 · 5475 in / 1366 out tokens · 23995 ms · 2026-05-10T15:35:52.132593+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Agile coverage for low-altitude aerial intelligent networks: A blended hyper-cellular solution,

    S. Zhou, B. Xie, D. Shen, W. Feng, Z. Jiang, and Z. Niu, “Agile coverage for low-altitude aerial intelligent networks: A blended hyper-cellular solution,”China Communications, vol. 22, no. 9, pp. 22–36, 2025

    work page 2025

  2. [2]

    Information-driven fast marching autonomous exploration with aerial robots,

    P. Zhong, B. Chen, S. Lu, X. Meng, and Y . Liang, “Information-driven fast marching autonomous exploration with aerial robots,”IEEE Robot. Autom. Lett., vol. 7, no. 2, pp. 810–817, 2022

    work page 2022

  3. [3]

    Soar: Simultaneous exploration and photograph- ing with heterogeneous uavs for fast autonomous reconstruction,

    M. Zhang, C. Feng, Z. Li, G. Zheng, Y . Luo, Z. Wang, J. Zhou, S. Shen, and B. Zhou, “Soar: Simultaneous exploration and photograph- ing with heterogeneous uavs for fast autonomous reconstruction,” in 2025 IEEE/RSJ Int. Conf. Intel. Robot. Sys. (IROS), 2025, pp. 10 975– 10 982

    work page 2025

  4. [4]

    Lightweight yet high-performance defect detector for uav-based large- scale infrastructure real-time inspection,

    B. Zhao, Q. Duan, G. Yang, J. Tang, Z. Song, J. Wen, X. Liu, Q. Li, L. Lei, J. Zhang, X. Chen, M. W. Mueller, and B. M. Chen, “Lightweight yet high-performance defect detector for uav-based large- scale infrastructure real-time inspection,” in2025 IEEE Int. Conf. Robot. Autom. (ICRA), 2025, pp. 13 675–13 682

    work page 2025

  5. [5]

    Perception-aware planning for quadrotor flight in unknown and feature-limited environments,

    C. Yu, Z. Lu, J. Mei, and B. Zhou, “Perception-aware planning for quadrotor flight in unknown and feature-limited environments,” in2025 IEEE/RSJ Int. Conf. Intel. Robot. Sys. (IROS), 2025, pp. 3533–3540

    work page 2025

  6. [6]

    Aircopbench: A benchmark for multi-drone collaborative embodied perception and reasoning,

    J. Zha, Y . Fan, T. Zhang, G. Chen, Y . Chen, C. Gao, and X. Chen, “Aircopbench: A benchmark for multi-drone collaborative embodied perception and reasoning,” inAAAI, 2026

    work page 2026

  7. [7]

    Flying in clutter on monocular rgb by learning in 3d radiance fields with domain adaptation,

    X. Huang, J. Li, T. Wu, X. Zhou, Z. Han, and F. Gao, “Flying in clutter on monocular rgb by learning in 3d radiance fields with domain adaptation,”IEEE Robotics and Automation Letters, pp. 1–8, 2026

    work page 2026

  8. [8]

    Stt-gs: Sample-then-transmit edge gaussian splatting with joint client selection and power control,

    Z. Li, X. Jin, G. Li, S. Wang, M. Wen, H. Arslan, D. Wing Kwan Ng, and C. Xu, “Stt-gs: Sample-then-transmit edge gaussian splatting with joint client selection and power control,”IEEE Trans. Cogn. Commun. Netw., vol. 12, pp. 4417–4432, 2026

    work page 2026

  9. [9]

    Low cubic metric reed-muller sequence design for pilot-less transmission,

    Y . Qin and R.-A. Pitaval, “Low cubic metric reed-muller sequence design for pilot-less transmission,”IEEE Commun. Lett., vol. 26, no. 2, pp. 364–368, 2022

    work page 2022

  10. [10]

    End-to-end learning for ofdm: From neural receivers to pilotless communication,

    F. Ait Aoudia and J. Hoydis, “End-to-end learning for ofdm: From neural receivers to pilotless communication,”IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1049–1063, 2022

    work page 2022

  11. [11]

    Adaptive end-to-end transceiver design for nextg pilot-free and cp-free wireless systems,

    J. Cheng, W. Chen, and B. Ai, “Adaptive end-to-end transceiver design for nextg pilot-free and cp-free wireless systems,”IEEE J. Sel. Areas Commun., vol. 44, pp. 3055–3069, 2026

    work page 2026

  12. [12]

    Unveiling the power of complex-valued transformers in wireless communications,

    Y . Leng, Q. Lin, L.-Y . Yung, J. Lei, Y . Li, and Y .-C. Wu, “Unveiling the power of complex-valued transformers in wireless communications,” IEEE Trans. Commun., vol. 74, pp. 612–627, 2026

    work page 2026

  13. [13]

    Sensing for free: Learn to localize more sources than antennas without pilots,

    W. Yu, K. B. Letaief, and L. Zheng, “Sensing for free: Learn to localize more sources than antennas without pilots,”IEEE J. Sel. Areas Commun., vol. 44, pp. 3285–3301, 2026

    work page 2026

  14. [14]

    Radio map-based beamforming assisted with reduced pilots,

    B. Yang, W. Wang, and W. Zhang, “Radio map-based beamforming assisted with reduced pilots,”IEEE Trans. Wireless Commun., vol. 24, no. 10, pp. 8878–8891, 2025

    work page 2025

  15. [15]

    Task-oriented image transmission for scene classification in unmanned aerial systems,

    X. Kang, B. Song, J. Guo, Z. Qin, and F. R. Yu, “Task-oriented image transmission for scene classification in unmanned aerial systems,”IEEE Trans. Commun., vol. 70, no. 8, pp. 5181–5192, 2022

    work page 2022

  16. [16]

    Edge collaborative gaussian splatting with integrated rendering and communication,

    Y . Wan, C. Liu, S. Wang, T. Zhang, J. J. Yu, K. Ye, D. Niyato, and C. Xu, “Edge collaborative gaussian splatting with integrated rendering and communication,” inIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026

    work page 2026

  17. [17]

    3d gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,”ACM Trans. Graph., vol. 42, no. 4, July 2023

    work page 2023

  18. [18]

    Communication efficient robotic mixed reality with gaussian splatting cross-layer optimization,

    C. Liu, H. Li, Z. Li, S. Wang, W. Xu, K. Ye, D. W. K. Ng, and C. Xu, “Communication efficient robotic mixed reality with gaussian splatting cross-layer optimization,”IEEE Trans. Cogn. Commun. Netw., vol. 12, pp. 1948–1962, 2026

    work page 1948

  19. [19]

    AI and ML for NR air interface in Rel-20 5G-Advanced,

    3GPP, “AI and ML for NR air interface in Rel-20 5G-Advanced,” NVIDIA, Tech. Rep. RP-242759, Dec 2024. [Online]. Available: https://www.3gpp.org/ftp/TSGRAN/TSGRAN/TSGR106/Docs/RP- 242759.zip

    work page 2024

  20. [20]

    Communication- efficient decentralized linear precoding for massive mu-mimo systems,

    X. Zhao, M. Li, Y . Liu, T.-H. Chang, and Q. Shi, “Communication- efficient decentralized linear precoding for massive mu-mimo systems,” IEEE Trans. Signal Process., vol. 71, pp. 4045–4059, 2023

    work page 2023

  21. [21]

    Wmmse-based joint transceiver design for multi-ris-assisted cell-free networks using hybrid csi,

    X. Pan, Z. Zheng, X. Huang, and Z. Fei, “Wmmse-based joint transceiver design for multi-ris-assisted cell-free networks using hybrid csi,”IEEE Trans. Wireless Commun., vol. 24, no. 9, pp. 7654–7669, 2025

    work page 2025

  22. [22]

    Gauu-scene v2: Assessing the reliability of image-based metrics with expansive lidar image dataset using 3dgs and nerf.arXiv preprint arXiv:2404.04880, 2024

    B. Xiong, N. Zheng, J. Liu, and Z. Li, “Gauu-scene v2: Assessing the reliability of image-based metrics with expansive lidar image dataset using 3dgs and nerf,” 2024. [Online]. Available: https://arxiv.org/abs/2404.04880

    work page arXiv 2024

  23. [23]

    Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs,

    H. Turki, D. Ramanan, and M. Satyanarayanan, “Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs,” inCVPR, June 2022, pp. 12 922–12 931

    work page 2022