An Efficient and High-Quality Mesh Reconstruction Method with Adaptive Visibility and Dynamic Refinement

Abstract

Image-based 3D reconstruction generates 3D mesh models from images and plays an important role in all walks of life. However, existing methods suffer from poor reconstruction quality and low reconstruction efficiency. To address this issue, we propose an improved optimization-based mesh reconstruction method with adaptive visibility reconstruction and dynamic photo-metric refinement. The adaptive visibility reconstruction adjusts soft visibility based on the observation and geometry structure of points to reconstruct details while suppressing noise in the rough mesh. The dynamic photo-metric refinement tunes the learning rate using historical gradients and stops to optimize converged triangles to speed up the mesh refinement. Experiments on BlendedMVS and real datasets showed that our method found a good balance between reconstruction quality and reconstruction efficiency. Compared with the state-of-the-art methods, OpenMVS and TDR, our method achieved higher reconstruction quality than OpenMVS and obtained competitive reconstruction quality with TDR, but required only one-third of the reconstruction time of OpenMVS and one-tenth of the reconstruction time of TDR. Our method balances reconstruction efficiency and reconstruction quality and can meet real-world application requirements.