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从三维离散点云表征到可学习的高斯渲染:数字化获取技术的30年变迁

黄惠

集成技术2026,Vol.15Issue(5):102-111,10.
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集成技术2026,Vol.15Issue(5):102-111,10.DOI:10.12146/j.issn.2095-3135.20260401001

从三维离散点云表征到可学习的高斯渲染:数字化获取技术的30年变迁

From Discrete 3D Point Cloud Representation to Learnable Gaussian Rendering:30-Year Evolution of Digital Acquisition Technologies

黄惠1

作者信息

  • 1. 深圳大学 计算机与软件学院 深圳 518060
  • 折叠

摘要

Abstract

Digital acquisition technology constructs digital replicas of the physical world and provides a three-dimensional infrastructure for virtual-real collaboration in artificial intelligence,with extensive applications in autonomous driving,surveying and remote sensing,intelligent robots,smart cities and other fields.This paper systematically reviews the evolution of 3D digital acquisition technologies over the past 30 years,dividing the development into three stages:classical geometric computing,multi-source heterogeneous data,and deep learning-driven technologies.It analyzes technical systems including discrete point clouds,mesh surfaces,multi-sourced data collection,data registration,3D mapping,real-time reconstruction,multi-view photogrammetry and neural rendering.Furthermore,this paper elaborates on the emerging stage of spatial intelligence integrating spatial computing and embodied intelligence.On this basis,this paper reviews existing technical frameworks and representative algorithms centered on popular research directions including physics simulation engines,bidirectional virtual-real alignment,and closed-loop perception-decision systems.Finally,we discuss development trends in content representation,virtual-real alignment and intelligent applications empowered by current multimodal large models,and provide systematic references for relevant researchers.

关键词

三维点云/高斯渲染/场景重建/神经隐式场/空间智能/虚实协同

Key words

3D point cloud/Gaussian Splatting/scene reconstruction/neural implicit field/spatial intelligence/virtual-real collaboration

分类

信息技术与安全科学

引用本文复制引用

黄惠..从三维离散点云表征到可学习的高斯渲染:数字化获取技术的30年变迁[J].集成技术,2026,15(5):102-111,10.

集成技术

2095-3135

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