Neural Implicit Surface Reconstruction Method Based on Multi-View Mixed Consistency Constraints

Multi-view 3D reconstruction based on neural implicit surface learning includes inherent ambiguities in representing the geometric shape and appearance of complex objects. Consequently, the fine geometric details of an object are prone to being lost in sparse texture areas, boundaries, and large smooth surfaces, making accurate recovery difficult. To address this issue, this study proposes a novel neural implicit surface reconstruction method based on multi-view mixed consistency constraints. This method uses Multi-View Stereo (MVS), multi-view photometric consistency, feature consistency, and volume rendering techniques to optimize the implicit surface representation, enabling the reconstruction of object models with fine geometric details. First, a dense point generation module based on MVS is proposed to supplement detail information in the sparse texture areas and boundaries of the object surface, achieving multi-view geometric optimization of the object surface. Second, a multi-view mixed consistency constraints module is introduced, which uses the Signed Distance Function (SDF) to locate the zero-level set. It applies multi-view photometric consistency constraints to impose geometric constraints on the smooth regions of the object, supervising the extracted implicit surface. Additionally, multi-view feature consistency constraints are applied to surface points at the zero-crossing of the linearly interpolated SDF, compensating for pixel matching errors in texture-sparse or structurally complex regions, thereby optimizing the object reconstruction model. Finally, volume rendering technology is applied to produce high-quality image renderings from the implicit SDF, enabling precise surface reconstruction of objects. Experimental results show that, compared to methods such as Colmap, the proposed method increases the Peak Signal-to-Noise Ratio (PSNR) by over 40.3% on the DTU dataset and successfully enables accurate surface reconstruction of the objects.