Research on multi-view 3D reconstruction algorithms based on geometry-aware convolutions

Multi-view stereo (MVS) 3D reconstruction is a fundamental task in computer vision, aiming to recover accurate scene geometry from multi-view images. However, existing methods continue to confront formidable challenges when handling complex scenes. To effectively address the above issues, this paper introduces an improved multi-view stereo-matching framework, AC-GoMVS. To mitigate the susceptibility of standard convolutions to depth noise at occlusion boundaries, we introduce an Adaptive Geometry-aware 3D Convolution (Agp-Conv3D) that exploits a dual-stream architecture comprising a principal pathway and a residual pathway. Furthermore, a dynamic attention mechanism is incorporated into the main path to adaptively adjust sampling positions of depth hypotheses, significantly improving edge detail reconstruction. In addition, a channel attention mechanism is embedded within the geometry consistency aggregation module to dynamically recalibrate the weight distribution of multi-level geometric features, addressing the feature mismatch issue caused by fixed-weight kernels in traditional 3D convolutions. Simultaneously, skip connections and residual links interlaced between the downsampling and upsampling pathways not only preserve rich fine-grained information, but also markedly enhance feature diversity. We evaluated our method on the DTU dataset and the Tanks and Temples benchmark. Experimental results show that, compared with the baseline model, the proposed approach achieves better reconstruction quality and stronger generalization ability.