UW-DNeRF:利用不确定性引导的深度监督和局部信息整合进行可变形软组织重建

Jiwei Shan;Zixin Zhang;Hao Li;Cheng-Tai Hsieh;Yirui Li;Wenhua Wu;Hesheng Wang
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引用次数: 0

摘要

从内窥镜视频中重建可变形的软组织是一项关键但具有挑战性的任务。利用深度先验,可变形隐式神经表征在这一领域取得了重大进展。然而,来自预训练深度估计模型的深度先验通常是粗糙的,不准确的深度监督会严重影响这些神经网络的性能。此外,现有的方法忽略了输入序列的局部相似性,这限制了它们在捕获局部细节和组织变形方面的有效性。在本文中,我们介绍了UW-DNeRF,一种利用神经辐射场进行高质量变形组织重建的新方法。我们提出了一种不确定性引导的深度监督策略,以减轻深度信息不准确的影响。这种策略放松了深度约束,释放了隐式神经表征的潜力。此外,我们还设计了一种基于本地窗口的信息共享方案。该方案采用局部窗口和关键帧变形网络构造具有局部感知的变形,增强了模型捕捉精细细节的能力。我们证明了我们的方法优于合成和体内内窥镜数据集的最先进方法。代码可从https://github.com/IRMVLab/UW-DNeRF获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UW-DNeRF: Deformable Soft Tissue Reconstruction With Uncertainty-Guided Depth Supervision and Local Information Integration
Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover, existing methods overlook local similarities in input sequences, which restricts their effectiveness in capturing local details and tissue deformations. In this paper, we introduce UW-DNeRF, a novel approach utilizing neural radiance fields for high-quality reconstruction of deformable tissues. We propose an uncertainty-guided depth supervision strategy to mitigate the impact of inaccurate depth information. This strategy relaxes hard depth constraints and unlocks the potential of implicit neural representations. In addition, we design a local window-based information sharing scheme. This scheme employs local window and keyframe deformation networks to construct deformations with local awareness and enhances the model’s ability to capture fine details. We demonstrate the superiority of our method over state-of-the-art approaches on synthetic and in vivo endoscopic datasets. Code is available at: https://github.com/IRMVLab/UW-DNeRF.
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