CALFNet: a light field reconstruction method based on channel attention mechanism

Light field reconstruction, a crucial domain in computer vision, generates 3D models of real-world scenes by combining multi-view images. Multi-view stereo technology is essential in this process, leveraging images from various angles to reconstruct 3D structures. It is widely used in applications such as cultural heritage preservation, robotics, and virtual reality. However, current methods often depend on rigid feature extraction and fusion techniques, restricting their capacity to fully exploit the rich multi-view data. This frequently results in blurred reconstructions, particularly in complex scenes with intricate edges and contours. To address these limitations, we propose CALFNet, a novel light field reconstruction method based on a channel attention mechanism. CALFNet combines this attention mechanism with a detail loss module, enabling the network to prioritize crucial channel information and edge features. This significantly enhances the accuracy and completeness of 3D reconstructions from multi-view images. Experiments on the DTU3Dreconstruction dataset demonstrate that CALFNet improves overall accuracy by 2.4% compared to existing methods. Additionally, tests on the Tanks and Temples dataset demonstrate the model's strong generalization capabilities, effectively handling diverse reconstruction tasks across various scenarios. This advancement paves the way for new applications in fields such as cultural heritage, robotics, and virtual reality.