Single-view 3D reconstruction method based on neural radiation field occlusion optimization

Single-view 3D reconstruction aims to restore the three-dimensional-dimensional geometry of an object or scene based on a single 2D image. The limited information provided by a single view often results in occlusion, , leading to blurred image features and inhibiting the accurate recovery of object appearance details. This paper introduces a framework, , called neural radiation field (NeRF ), that effectively addresses the occlusion problem by utilizing both global and local context information of the image. The proposed approach employs the Vision Transformer to capture long-range-range correlations and learn global features from the image. The Vision Transformer is combined with the SE channel attention mechanism module to prevent information loss and redundancy across multiple layers. Additionally, , a convolutional neural network is utilized to extract pixel-aligned local image features. The dilated convolution of the dilated pyramid pooling structure is employed to increase the receptive field, , extract multi-scale context information, , and provide more details for restoring occluded areas. Lastly, , a density feature aggregation module based on the Transformer architecture is designed to minimize inaccuracies in density prediction due to occlusion. Experimental results on the ShapeNet-NMR-NMR dataset demonstrate the method's 's ability to produce new views with enhanced details and exhibit strong generalization capabilities when applied to unseen objects.