Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations
研究提出一种计算感知的多任务协议，用于评估隐私增强技术在模型训练中的效果，通过轻量级代理任务解决仅依赖图像分类评估的局限性。
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RPC-GS: Gaussian Splatting with native RPC Rendering for Satellite Imagery
This work addresses the geometric reconstruction errors introduced by existing Gaussian splatting methods that rely on perspective or affine camera approximations of Rational Polynomial Coefficient (RPC) models. We propose, for the first time, a Gaussian splatting framework natively supporting RPC cameras, directly projecting Gaussian means and covariances through the RPC model to eliminate approximation errors. Our key innovations include integrating the RPC model into the splatting pipeline, designing a Jacobian-based robust covariance projection method, and introducing ray-based depth modeling to overcome the lack of explicit depth in RPC formulations. Evaluated on the DFC2019 and IARPA2016 datasets, our approach reduces mean elevation errors by 29.6%/63.8% and 9.9%/37.9%, respectively, compared to conventional methods, significantly advancing 3D reconstruction accuracy.
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Efficient representation of 3D spatial data for defense-related applications
To address the challenge of balancing geometric accuracy and visual fidelity in large-scale 3D spatial data for defense applications, this paper proposes a hierarchical hybrid representation architecture integrating classical geometric modeling with neural rendering. The architecture employs triangle meshes and voxel grids to ensure foundational geometric fidelity, while leveraging 3D Gaussian splatting and Neural Radiance Fields (NeRF) for high-fidelity photorealistic rendering at the upper layer. A unified scene management framework enables multi-granularity co-optimization across representations. Compared to purely geometric or purely neural approaches, our method achieves significant improvements: 2.1× acceleration in computational efficiency and +3.7 dB PSNR gain in rendering quality—particularly beneficial for line-of-sight analysis, physics-based simulation, and real-time visualization. It supports scalable modeling and interactive rendering of scenes with up to hundreds of millions of polygons, establishing a new paradigm for military digital twins that simultaneously delivers geometric precision, computational efficiency, and visual realism.
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Semantic Neural Radiance Fields for Multi-Date Satellite Data
Dynamic objects (e.g., vehicles) in multi-temporal satellite imagery cause cross-temporal geometric inconsistencies and semantic label noise, degrading 3D semantic reconstruction quality. Method: We propose a semantics-enhanced Satellite NeRF framework: (i) the first integration of pixel-wise semantic supervision into satellite NeRF, enabling joint semantic-geometric optimization; (ii) a multi-temporal radiometric consistency modeling module to mitigate color drift; and (iii) a self-supervised label purification strategy to improve robustness against noisy semantic annotations. Contributions/Results: We release the first manually annotated multi-view satellite semantic dataset; achieve state-of-the-art performance in both semantic segmentation accuracy and 3D reconstruction PSNR; and significantly enhance reconstruction fidelity of dynamic objects while suppressing temporal artifacts. Our code and dataset are publicly available.
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Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations
研究提出一种计算感知的多任务协议，用于评估隐私增强技术在模型训练中的效果，通过轻量级代理任务解决仅依赖图像分类评估的局限性。
0 citationsRead paper
RPC-GS: Gaussian Splatting with native RPC Rendering for Satellite Imagery
This work addresses the geometric reconstruction errors introduced by existing Gaussian splatting methods that rely on perspective or affine camera approximations of Rational Polynomial Coefficient (RPC) models. We propose, for the first time, a Gaussian splatting framework natively supporting RPC cameras, directly projecting Gaussian means and covariances through the RPC model to eliminate approximation errors. Our key innovations include integrating the RPC model into the splatting pipeline, designing a Jacobian-based robust covariance projection method, and introducing ray-based depth modeling to overcome the lack of explicit depth in RPC formulations. Evaluated on the DFC2019 and IARPA2016 datasets, our approach reduces mean elevation errors by 29.6%/63.8% and 9.9%/37.9%, respectively, compared to conventional methods, significantly advancing 3D reconstruction accuracy.
0 citationsRead paper
Efficient representation of 3D spatial data for defense-related applications
To address the challenge of balancing geometric accuracy and visual fidelity in large-scale 3D spatial data for defense applications, this paper proposes a hierarchical hybrid representation architecture integrating classical geometric modeling with neural rendering. The architecture employs triangle meshes and voxel grids to ensure foundational geometric fidelity, while leveraging 3D Gaussian splatting and Neural Radiance Fields (NeRF) for high-fidelity photorealistic rendering at the upper layer. A unified scene management framework enables multi-granularity co-optimization across representations. Compared to purely geometric or purely neural approaches, our method achieves significant improvements: 2.1× acceleration in computational efficiency and +3.7 dB PSNR gain in rendering quality—particularly beneficial for line-of-sight analysis, physics-based simulation, and real-time visualization. It supports scalable modeling and interactive rendering of scenes with up to hundreds of millions of polygons, establishing a new paradigm for military digital twins that simultaneously delivers geometric precision, computational efficiency, and visual realism.
0 citationsRead paper
Semantic Neural Radiance Fields for Multi-Date Satellite Data
Dynamic objects (e.g., vehicles) in multi-temporal satellite imagery cause cross-temporal geometric inconsistencies and semantic label noise, degrading 3D semantic reconstruction quality. Method: We propose a semantics-enhanced Satellite NeRF framework: (i) the first integration of pixel-wise semantic supervision into satellite NeRF, enabling joint semantic-geometric optimization; (ii) a multi-temporal radiometric consistency modeling module to mitigate color drift; and (iii) a self-supervised label purification strategy to improve robustness against noisy semantic annotations. Contributions/Results: We release the first manually annotated multi-view satellite semantic dataset; achieve state-of-the-art performance in both semantic segmentation accuracy and 3D reconstruction PSNR; and significantly enhance reconstruction fidelity of dynamic objects while suppressing temporal artifacts. Our code and dataset are publicly available.
0 citationsRead paper