Multi-view Stereo 3D Edge Reconstruction

Abstract

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 linesegments, 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 Edge-Graph3D 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.

Enhancing Geometric Edge Details in MVS Reconstruction

“…Bódis-Szomorú et al (2015) proposed an approach for large scale urban reconstruction for edge preserving mesh reconstruction enforcing the Delaunay Triangulation using CDT (Botsch et al, 2010) on a 2D base mesh. Bignoli et al (2018) presented an approach to detect both straight and curved edges and support thus the reconstruction of 3D meshes from sparse point clouds. Other approaches combine primitives for regular parts of the scene and meshes for the irregular ones (Lafarge et al, 2010).…”

“…We consider two different state-of-the-art 3D edge extraction methods (Figure 1), namely Line3D++ (Hofer et al, 2016) and EdgeGraph3D (Bignoli et al, 2018) and discuss their results and efficiency.…”

“…These erroneous reconstructed lines may not affect the abstract representation of the scene alone (Hofer et al, 2016), yet may introduce a significant error in the meshing step. Edge extraction with EdgeGraph3D: EdgeGraph3D algorithm (Bignoli et al, 2018) uses a-priori detected edges coming from standard edge detection algorithms along with the SfM output, i.e. camera poses and sparse point cloud to subsequently project the salient edges in 3D.…”

“…Our method uses dense point clouds and edge information in a joint fashion to support the edge-aware mesh reconstruction inspired by Bignoli et al (2018) and Bódis-Szomorú et al (2015).…”

True Orthophoto Generation Based on Unmanned Aerial Vehicle Images Using Reconstructed Edge Points

“…As a result, some parts of the lines could not be reconstructed. In other works, camera poses are given, and LSs are matched and reconstructed using epipolar and 3D geometry constraints (Jain et al., 2010; Hofer et al., 2017; Bignoli et al., 2018).…”