SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos
This work addresses the need for automated wound assessment in healthcare, but it is incremental as it focuses on benchmarking existing methods rather than proposing a new solution.
The paper tackles the problem of 3D wound reconstruction from consumer-grade videos for clinical assessment by introducing the SALVE dataset and evaluating state-of-the-art methods, finding that neural rendering approaches show promise over photogrammetry for providing smooth surfaces suitable for precise measurements.
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/.