Institution profile

Hubei Luojia Laboratory

Academic institutionasia · cn
Research library15linked papers
Opportunities0open roles
Selected work

Representative Papers

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

Jul 01, 2026

Existing feedforward 3D foundation models, constrained by central perspective projection, struggle to accommodate the push-broom imaging geometry of satellites, limiting their applicability in multi-view satellite 3D reconstruction. This work proposes a lightweight adaptation framework that requires no fine-tuning of the backbone network: it selects an optimal view sequence through geometric consistency constraints, parameterizes push-broom rays into geometric tokens using rational function models, and introduces a ray-direction-aware adapter to inject these tokens into a frozen Transformer backbone. This approach achieves, for the first time, an effective integration of physical imaging geometry with deep feedforward architectures, significantly enhancing accuracy and robustness in digital surface model (DSM) generation and demonstrating the critical role of explicit geometric embedding and optimized view selection.

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Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset

Jun 16, 2026

Cloud contamination severely compromises the reliability of optical remote sensing imagery for near-real-time land use and land cover (LULC) mapping. To address this challenge, this work proposes CloudLULC-Net, an end-to-end heterogeneous SAR-optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 images together with temporally adjacent Sentinel-1 SAR data, accompanied by the introduction of a large-scale benchmark dataset, CloudLULC-Set. The method innovatively integrates optical reliability modulation, adaptive heterogeneous information aggregation, a unified semantic mapping Transformer, and semantic anchor-guided optimization to effectively mitigate semantic uncertainty under cloud occlusion. Experimental results demonstrate that the model achieves 86.60% overall accuracy, 83.29% F1 score, and 73.51% mIoU on CloudLULC-Set, significantly outperforming both reconstruction-first and existing end-to-end approaches while maintaining robust performance across varying cloud coverage levels.

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Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning

May 12, 2026

This study addresses the limitations of low-cost MEMS inertial measurement units (IMUs), whose hardware-constrained accuracy often falls short of high-precision navigation requirements. To overcome this challenge, the work introduces a conditional diffusion-based generative framework—the first to apply diffusion models to IMU signal enhancement—leveraging a U-Net architecture to synthesize high-fidelity virtual IMU signals. The model takes high-accuracy IMU data as a prior and low-cost IMU measurements as conditional inputs. By doing so, it effectively transcends the performance ceiling of low-cost sensors, yielding significantly improved localization and orientation estimates compared to raw measurements. Experimental validation in airborne mapping demonstrates that the enhanced signals produce denser, more consistent point clouds, confirming the method’s efficacy in multi-sensor integrated navigation systems.

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Recent publications

Latest Papers

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

Jul 01, 2026

Existing feedforward 3D foundation models, constrained by central perspective projection, struggle to accommodate the push-broom imaging geometry of satellites, limiting their applicability in multi-view satellite 3D reconstruction. This work proposes a lightweight adaptation framework that requires no fine-tuning of the backbone network: it selects an optimal view sequence through geometric consistency constraints, parameterizes push-broom rays into geometric tokens using rational function models, and introduces a ray-direction-aware adapter to inject these tokens into a frozen Transformer backbone. This approach achieves, for the first time, an effective integration of physical imaging geometry with deep feedforward architectures, significantly enhancing accuracy and robustness in digital surface model (DSM) generation and demonstrating the critical role of explicit geometric embedding and optimized view selection.

0 citationsRead paper

Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset

Jun 16, 2026

Cloud contamination severely compromises the reliability of optical remote sensing imagery for near-real-time land use and land cover (LULC) mapping. To address this challenge, this work proposes CloudLULC-Net, an end-to-end heterogeneous SAR-optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 images together with temporally adjacent Sentinel-1 SAR data, accompanied by the introduction of a large-scale benchmark dataset, CloudLULC-Set. The method innovatively integrates optical reliability modulation, adaptive heterogeneous information aggregation, a unified semantic mapping Transformer, and semantic anchor-guided optimization to effectively mitigate semantic uncertainty under cloud occlusion. Experimental results demonstrate that the model achieves 86.60% overall accuracy, 83.29% F1 score, and 73.51% mIoU on CloudLULC-Set, significantly outperforming both reconstruction-first and existing end-to-end approaches while maintaining robust performance across varying cloud coverage levels.

0 citationsRead paper

Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning

May 12, 2026

This study addresses the limitations of low-cost MEMS inertial measurement units (IMUs), whose hardware-constrained accuracy often falls short of high-precision navigation requirements. To overcome this challenge, the work introduces a conditional diffusion-based generative framework—the first to apply diffusion models to IMU signal enhancement—leveraging a U-Net architecture to synthesize high-fidelity virtual IMU signals. The model takes high-accuracy IMU data as a prior and low-cost IMU measurements as conditional inputs. By doing so, it effectively transcends the performance ceiling of low-cost sensors, yielding significantly improved localization and orientation estimates compared to raw measurements. Experimental validation in airborne mapping demonstrates that the enhanced signals produce denser, more consistent point clouds, confirming the method’s efficacy in multi-sensor integrated navigation systems.

0 citationsRead paper