Improved Mesh Reconstruction With an Edge Quality Enhancement Using Multiple Inward Depth Streams

Abstract

This paper presents a complete 3D model reconstruction of an object with edge quality enhancements using multiple inward depth sensors to create closed 3D model. In the reconstruction pipeline, a pattern of incorrect depth information was consistently observed at the edges of the mesh generated by each sensor stream, which we refer to in this paper as a ‘‘drift-effect’’. In order to mitigate this, we introduced a filtering approach with a localized threshold value that is used to remove drift faces from a mesh. We also present a mesh stitching technique incorporating Laplacian mesh smoothing to generate a closed 3D model from the smoothened multi-view meshes. The primary objective of this research was to implement a system that could capture a static physical object with a minimum scan time and at a low cost while retaining accurate details in the model. For the demonstration, we used four Intel RealSense D435 depth sensors to capture a clothing article that can be imported into a virtual dressing room application. We captured the entire object within three seconds, which is quicker than traditional techniques such as table rotation and sensor rotation. The final results indicate that the system is able to provide a satisfactory reconstruction of a clothing model which can be used in a live virtual dressing room application.