  This paper introduces a novel method for reconstructing meshes from sparse
point clouds by predicting edge connection. Existing implicit methods usually
produce superior smooth and watertight meshes due to the isosurface extraction
algorithms~(e.g., Marching Cubes). However, these methods become memory and
computationally intensive with increasing resolution. Explicit methods are more
efficient by directly forming the face from points. Nevertheless, the challenge
of selecting appropriate faces from enormous candidates often leads to
undesirable faces and holes. Moreover, the reconstruction performance of both
approaches tends to degrade when the point cloud gets sparse. To this end, we
propose MEsh Reconstruction via edGE~(MergeNet), which converts mesh
reconstruction into local connectivity prediction problems. Specifically,
MergeNet learns to extract the features of candidate edges and regress their
distances to the underlying surface. Consequently, the predicted distance is
utilized to filter out edges that lay on surfaces. Finally, the meshes are
reconstructed by refining the triangulations formed by these edges. Extensive
experiments on synthetic and real-scanned datasets demonstrate the superiority
of MergeNet to SoTA explicit methods.
