计算机工程2024，Vol.50Issue(10)：334-341,8.DOI:10.19678/j.issn.1000-3428.0068291
基于锥形追踪和网络分解的NeRF三维重建方法
NeRF 3D Reconstruction Method Based on Cone Tracking and Network Decomposition
摘要
Abstract
In computer vision,Neural Radiance Fields(NeRF)define processes that use spatial coordinates or other dimensions,such as time and camera pose,as input and simulate the objective function through a Multi-Layer Perceptron(MLP)network to generate the target scalar(color and depth).NeRF reconstructs 3D scenes well but blurs or distorts different resolutions and trains them slowly.To solve these two issues,this study proposes a NeRF 3D reconstruction method based on cone tracking and network decomposition.First,the cone-tracking method is used to project a cone for each pixel;the projected cone is cut into a series of cones,characterized along the cone,and the blur or artifact effect is reduced by efficiently rendering the anti-aliasing cone.To shorten the training time,the neural network of the original NeRF receiving five-dimensional data is decomposed into two networks using the network decomposition method,which effectively shortens the training time.Experimental results show that the proposed method improves the Peak Signal-to-Noise Ratio(PSNR)by 14.4％-24.6％compared with NeRF,F2-NeRF,and other algorithms in NeRF_Synthetic,LLFF,and Multiresolution datasets.The training time is also reduced,which allows the reconstruction of richer detailed features,better visual effects,and faster training speed.关键词
神经辐射场/多层感知机/三维重建/神经网络/隐式重建/锥形追踪/网络分解Key words
Neural Radiation Field(NeRF)/Multi-Layer Perceptron(MLP)/3D reconstruction/neural network/implicit reconstruction/cone tracking/network decomposition分类
信息技术与安全科学引用本文复制引用
景维鹏,王源锋,李超..基于锥形追踪和网络分解的NeRF三维重建方法[J].计算机工程,2024,50(10):334-341,8.基金项目
黑龙江省"揭榜挂帅"科技攻关项目(2022ZXJ04A03). （2022ZXJ04A03）
