计算机与现代化Issue(12)：46-53,8.DOI:10.3969/j.issn.1006-2475.2025.12.007
多视图IM-NET三维目标精细化重建
Multi-view IM-NET for Fine Reconstruction of 3D Object
摘要
Abstract
Implicit net(IM-NET)converts the task of reconstructing three-dimensional(3D)objects into a classification issue concerning whether sampled points in space are on or inside the object's surface,effectively conserving computational and stor-age resources.However,IM-NET can only reconstruct 3D objects from a single view,and the limited amount of target informa-tion available in a single view,especially the missing information from occluded parts,leads to insufficient reconstruction accu-racy.This paper extends IM-NET to adapt multi-view inputs by employing an attention module to fuse extracted features,thereby obtaining more complete target features and enhancing 3D reconstruction accuracy.Considering that the target surface generated by the Marching Cubes algorithm is not smooth enough when converting implicit expression into explicit mesh represen-tation,this paper utilizes a mesh refinement algorithm to refine the reconstructed targets iteratively,achieving the refined recon-struction.Experimental results on the ShapNet dataset indicate that compared with single-view IM-NET and other multi-view re-construction methods,the proposed multi-view IM-NET reconstructs 3D targets more complete and smooth,and the average in-tersection and union ratios of targets are significantly improved.Additionally,visualization effects show that the refined targets have richer details and smoother surfaces than those without refinement.关键词
深度学习/三维重建/IM-NET/多视图Key words
deep learning/3D reconstruction/IM-NET/multi-view分类
信息技术与安全科学引用本文复制引用
刘健龙,岑颖,许斌,焦旋..多视图IM-NET三维目标精细化重建[J].计算机与现代化,2025,(12):46-53,8.基金项目
江西省自然科学基金资助项目(20224BAB202002) （20224BAB202002）
