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Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data

Autor: Wang, An, Yin, Haochen, Cui, Beilei, Xu, Mengya, Ren, Hongliang

Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is compromised by image distortions common in surgical settings. To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic d

Externí odkaz: http://arxiv.org/abs/2409.16063

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Registering Neural 4D Gaussians for Endoscopic Surgery

Autor: Huang, Yiming, Cui, Beilei, Kei, Ikemura, Zhang, Jiekai, Bai, Long, Ren, Hongliang

The recent advance in neural rendering has enabled the ability to reconstruct high-quality 4D scenes using neural networks. Although 4D neural reconstruction is popular, registration for such representations remains a challenging task, especially for

Externí odkaz: http://arxiv.org/abs/2407.20213

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Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings

Autor: Tang, Ruijie, Cui, Beilei, Ren, Hongliang

As the significance of simulation in medical care and intervention continues to grow, it is anticipated that a simplified and low-cost platform can be set up to execute personalized diagnoses and treatments. 3D Slicer can not only perform medical ima

Externí odkaz: http://arxiv.org/abs/2406.13048

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EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera

Autor: Cui, Beilei, Islam, Mobarakol, Bai, Long, Wang, An, Ren, Hongliang

Depth estimation plays a crucial role in various tasks within endoscopic surgery, including navigation, surface reconstruction, and augmented reality visualization. Despite the significant achievements of foundation models in vision tasks, including

Externí odkaz: http://arxiv.org/abs/2405.08672

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Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting

Autor: Huang, Yiming, Cui, Beilei, Bai, Long, Guo, Ziqi, Xu, Mengya, Islam, Mobarakol, Ren, Hongliang

In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence for their

Externí odkaz: http://arxiv.org/abs/2401.16416

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Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery

Autor: Cui, Beilei, Islam, Mobarakol, Bai, Long, Ren, Hongliang

Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g.

Externí odkaz: http://arxiv.org/abs/2401.06013

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Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation

Autor: Cui, Beilei, Zhang, Minqing, Xu, Mengya, Wang, An, Yuan, Wu, Ren, Hongliang

Noisy label problems are inevitably in existence within medical image segmentation causing severe performance degradation. Previous segmentation methods for noisy label problems only utilize a single image while the potential of leveraging the correl

Externí odkaz: http://arxiv.org/abs/2307.05898

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