Quantitative assessment is essential to ensure correct diagnosis and effective treatment of chronic wounds. So far, devices with depth cameras and infrared sensors have been used for the computer-aided diagnosis of cutaneous wounds. However, these devices have limited accessibility and usage. On the other hand, smartphones are commonly available, and threedimensional (3D) reconstruction using smartphones can be an important tool for wound assessment. In this paper, we analyze various open source libraries for smartphone-based 3D reconstruction of wounds. For this, point clouds are obtained from cutaneous wound regions using Google ARCore and Structure from Motion (SfM) libraries. These point clouds are subjected to de-noising filters to remove outliers and to improve the density of the point cloud. Subsequently, surface reconstruction is performed on the point cloud to generate a 3D model. Six different mesh-reconstruction algorithms namely Delaunay triangulation, convex hull, point crust, Poisson surface reconstruction, alpha complex, and marching cubes are considered. The performances are evaluated using the quality metrics such as complexity, the density of point clouds, the accuracy of depth information and the efficacy of the reconstruction algorithm. The result shows that the point clouds are able to perform 3D reconstruction of wounds using open source libraries. It is found that the point clouds obtained from SfM have higher density and accuracy as compared to ARCore. Comparatively, the Poisson surface reconstruction is found to be the best algorithm for effective 3D reconstruction from the point clouds. However, research is still required on the techniques to enhance the quality of point clouds obtained through the smartphones and to reduce the computational cost associated with point cloud based 3D-reconstruction.