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Multi-view computational scene modelling

多视图计算场景建模

基本信息

批准号：
1815219
负责人：
金额：
--
依托单位：
University of Surrey
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1815219
关键词：
Multi view computational scene modelling

项目摘要

In recent years, we have witnessed dramatic improvements in 3D reconstruction accuracy. With the advent of low cost depth sensors and more robust multi-view reconstruction algorithms, scene modelling techniques have become popularised and no longer limited to lab environments. However, despite their flexibility, existing techniques remain limited to scenes with simple surface reflectance properties (mostly diffuse). While more complex photometric approaches have been proposed to deal with such scenes, they tend to require expensive specialist equipment that cannot be deployed outside of the lab.This project will fill this gap by developing more general and versatile digitisation techniques that can be applied to natural scenes with complex reflectance captured using consumer hardware. Building on recent advances in computer vision and computational photography, the project will introduce novel acquisition hardware and algorithms to infer depth together with other important attributes such as surface normal and BRDF. To further push the limits of modelling accuracy, the research will also consider complex physical phenomena which degrade reconstruction accuracy, such as sub-surface scattering and inter-reflections, and will explore how algorithms can be made more resilient to these effects.The application of this research will be demonstrated in the context of the creative industries such as film, gaming and cultural heritage which all require the ability to create photo-realistic digital copies of assets. A particular emphasis will be placed on modelling natural or man-made scenes which have a complex surface reflectance (e.g. glossy materials). It is anticipated that this technology will find applications in many other areas of industry which require accurate modelling such as in robotics or the manufacturing industry.

近年来，我们见证了 3D 重建精度的显着提高。随着低成本深度传感器和更强大的多视图重建算法的出现，场景建模技术已经变得普及，不再局限于实验室环境。然而，尽管具有灵活性，现有技术仍然仅限于具有简单表面反射特性（主要是漫反射）的场景。虽然已经提出了更复杂的光度测量方法来处理此类场景，但它们往往需要昂贵的专业设备，而这些设备无法在实验室之外部署。该项目将通过开发更通用和通用的数字化技术来填补这一空白，这些技术可应用于使用消费类硬件捕获的具有复杂反射率的自然场景。基于计算机视觉和计算摄影的最新进展，该项目将引入新颖的采集硬件和算法来推断深度以及其他重要属性，例如表面法线和 BRDF。为了进一步突破建模精度的极限，该研究还将考虑会降低重建精度的复杂物理现象，例如次表面散射和相互反射，并将探索如何使算法对这些效应更具弹性。这项研究的应用将在电影、游戏和文化遗产等创意产业的背景下进行论证，这些产业都需要创建资产的照片级真实数字副本的能力。将特别强调对具有复杂表面反射率（例如光泽材料）的自然或人造场景进行建模。预计这项技术将在需要精确建模的许多其他工业领域得到应用，例如机器人或制造业。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Family of Approaches for Full 3D Reconstruction of Objects with Complex Surface Reflectance

DOI：
10.1007/s11263-023-01795-w
发表时间：
2023-06
期刊：
International Journal of Computer Vision
影响因子：
19.5
作者：
Gianmarco Addari;Jean-Yves Guillemaut
通讯作者：
Gianmarco Addari;Jean-Yves Guillemaut

Towards Globally Optimal full 3D reconstruction of scenes with complex reflectance using Helmholtz Stereopsis

DOI：
10.1145/3359998.3369410
发表时间：
2019-12
期刊：
Proceedings of the 16th ACM SIGGRAPH European Conference on Visual Media Production
影响因子：
0
作者：
Gianmarco Addari;Jean-Yves Guillemaut
通讯作者：
Gianmarco Addari;Jean-Yves Guillemaut

An MRF Optimisation Framework for Full 3D Helmholtz Stereopsis