SALVE：基于消费级视频的伤口三维重建基准
SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos
AI总结：
本文提出SALVE数据集，系统评估了从消费级视频进行三维伤口重建的方法，发现传统摄影测量存在表面光滑度不足问题，而神经渲染方法在临床伤口测量中更具潜力。
摘要：
慢性伤口管理是一项全球性挑战，通过采用基于消费级视频的临床伤口自动评估系统可以缓解这一问题。二维图像分析方法不足以处理伤口的三维特征，而现有利用三维重建方法的研究尚未得到充分评估。为填补这一空白，本文对基于消费级视频的三维伤口重建进行了全面研究。具体而言，我们引入了SALVE数据集，该数据集包含使用不同相机拍摄的逼真伤口模型视频记录。利用该数据集，我们评估了当前最先进的三维重建方法的准确性和精度，涵盖从传统摄影测量流程到先进神经渲染方法。在实验中，我们观察到摄影测量方法无法提供适合伤口精确临床测量的光滑表面。神经渲染方法在解决这一问题方面展现出潜力，推动了该技术在伤口护理实践中的应用。我们鼓励读者访问项目页面：https://remichierchia.github.io/SALVE/。
英文摘要：
Managing chronic wounds is a global challenge that can be alleviated by the adoption of automatic systems for clinical wound assessment from consumer-grade videos. While 2D image analysis approaches are insufficient for handling the 3D features of wounds, existing approaches utilizing 3D reconstruction methods have not been thoroughly evaluated. To address this gap, this paper presents a comprehensive study on 3D wound reconstruction from consumer-grade videos. Specifically, we introduce the SALVE dataset, comprising video recordings of realistic wound phantoms captured with different cameras. Using this dataset, we assess the accuracy and precision of state-of-the-art methods for 3D reconstruction, ranging from traditional photogrammetry pipelines to advanced neural rendering approaches. In our experiments, we observe that photogrammetry approaches do not provide smooth surfaces suitable for precise clinical measurements of wounds. Neural rendering approaches show promise in addressing this issue, advancing the use of this technology in wound care practices. We encourage the readers to visit the project page: https://remichierchia.github.io/SALVE/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
