Multi-View Stereo 3D Edge Reconstruction
This addresses a specific limitation in 3D reconstruction for computer vision applications, offering an incremental improvement over prior methods.
The paper tackles the problem of reconstructing 3D edges in multi-view stereo by proposing EdgeGraph3D, which recovers both straight and curved edges from unordered images and improves surface accuracy in challenging conditions like illumination changes and reflections.
This paper presents a novel method for the reconstruction of 3D edges in multi-view stereo scenarios. Previous research in the field typically relied on video sequences and limited the reconstruction process to either straight line-segments, or edge-points, i.e., 3D points that correspond to image edges. We instead propose a system, denoted as EdgeGraph3D, able to recover both straight and curved 3D edges from an unordered image sequence. A second contribution of this work is a graph-based representation for 2D edges that allows the identification of the most structurally significant edges detected in an image. We integrate EdgeGraph3D in a multi-view stereo reconstruction pipeline and analyze the benefits provided by 3D edges to the accuracy of the recovered surfaces. We evaluate the effectiveness of our approach on multiple datasets from two different collections in the multi-view stereo literature. Experimental results demonstrate the ability of EdgeGraph3D to work in presence of strong illumination changes and reflections, which are usually detrimental to the effectiveness of classical photometric reconstruction systems.