Mixer-NeRF: Research on 3D Reconstruction Methods of Neural Radiance Fields Based on Hybrid Spatial Feature Information

Compared to traditional 3D reconstruction techniques, Neural Radiance Fields (NeRF) have demonstrated significant performance advantages in implicit 3D reconstruction. However, simple multi-layer perceptron (MLP) models often result in blurry details in the reconstructed 3D scenes due to their lack of necessary local information capture during the sampling process. To address this issue, this paper proposes a joint learning method based on hybrid spatial feature information to enhance detail reconstruction. First, after the position encoding output, a hybrid spatial feature information learning module (MLCA) is added before entering the MLP network and before the input direction information, capturing spatial feature information and mixing the channels of these features to obtain the features of different channels at each spatial location. Then, a squeeze-and-excitation network module is introduced between the NeRF sampling and inference layers to filter out high-weight information. Experimental results show that the proposed 3D reconstruction method based on hybrid spatial feature information can effectively improve NeRF’s reconstruction performance. It is applicable to complex, real, multi-view scenarios, especially showing the best performance under high-texture and complex lighting conditions. Compared to NeRF, this method improves the learning of perceptual image block similarity by more than 30%.