Total Differential Photometric Mesh Refinement with Self-Adapted Mesh Denoising

Abstract

Variational mesh refinement is a crucial step in multiview 3D reconstruction. Existing algorithms either focus on recovering mesh details or focus on suppressing noise. Approaches with consideration of both are lacking. To address this limitation, we proposed a new variational mesh refinement method named total differential mesh refinement (TDR), which mainly included two improvements. First, the traditional partial-differential photo-consistency gradient used in the variational mesh refinement method was replaced by the proposed total-differential photo-consistency gradient. With consideration of the photo-consistency correlation between adjacent pixels, our method can make photo-consistency achieve a more effective convergence than traditional approaches. Second, we introduced the bilateral normal filter with a novel self-adaptive mesh denoising strategy into the variational mesh refinement. This strategy maintains a balance between detail preservation and effective denoising via the zero-normalized cross-correlation (ZNCC) map. Various experiments demonstrated that our method is superior to traditional variational mesh refinement approaches in both accuracy and denoising effect. Moreover, compared with the mesh generated by open-source and commercial software (Context Capture), our meshes are more detailed, regular, and smooth.