Multiview Stereo (MVS) reconstruction techniques have made significant advancements with the development of deep learning. However, their performance often deteriorates in low-light conditions, where feature extraction and matching become challenging. Traditional image enhancement solutions are insufficient for MVS tasks in low illumination, relying on manual adjustments. We introduce an end-to-end MVS framework incorporating a diffusion-based image enhancement algorithm with MVS to build an end-to-end framework for improving the performance of MVS in low-light conditions. This integration improves color rendering and visualization of 3D reconstructions and slightly enhances geometric shapes. Our method uses a feature adapter to integrate the enhanced images from the Low-light Diffusion model into CasMVSNet, refining the feature maps in poorly lit environments. Validation on the DTU and Tanks and Temples datasets demonstrates our model’s robustness and generalizability across various lighting conditions and MVS pipelines, including GeoMVSNet and MVSNet. Our approach simplifies the training process by requiring only the training of an adapter rather than a multi-view image enhancement model, underscoring the effectiveness of incorporating image enhancement into learning-based MVS frameworks for low-light conditions.