Suppr超能文献

基于嘉当联络的局部可变形三维重建

Local Deformable 3D Reconstruction with Cartan's Connections.

作者信息

Parashar Shaifali, Pizarro Daniel, Bartoli Adrien

出版信息

IEEE Trans Pattern Anal Mach Intell. 2020 Dec;42(12):3011-3026. doi: 10.1109/TPAMI.2019.2920821. Epub 2020 Nov 3.

Abstract

3D reconstruction of deformable objects using inter-image visual motion from monocular images has been studied under Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM). Most methods have been developed for simple deformation models, primarily isometry. They may treat a surface as a discrete set of points and draw constraints from the points only or they may use a non-parametric representation and use both points and differentials to express constraints. We propose a differential framework based on Cartan's theory of connections and moving frames. It is applicable to SfT and NRSfM, and to deformation models other than isometry. It utilises infinitesimal-level assumptions on the surface's geometry and mappings. It has the following properties. 1) It allows one to derive existing solutions in a simpler way. 2) It models SfT and NRSfM in a unified way. 3) It allows us to introduce a new skewless deformation model and solve SfT and NRSfM for it. 4) It facilitates a generic solution to SfT which does not require deformation modeling. Our framework is complete: it solves deformable 3D reconstruction for a whole class of algebraic deformation models including isometry. We compared our solutions with the state-of-the-art methods and show that ours outperform in terms of both accuracy and computation time.

摘要

利用单目图像中的图像间视觉运动对可变形物体进行三维重建,已在基于模板的形状(SfT)和非刚性运动结构(NRSfM)下展开研究。大多数方法是针对简单变形模型开发的,主要是等距模型。它们可能将表面视为离散的点集,仅从这些点得出约束条件,或者可能使用非参数表示法,并同时使用点和微分来表达约束。我们提出了一个基于嘉当联络理论和活动标架的微分框架。它适用于SfT和NRSfM,以及等距模型之外的其他变形模型。它利用了关于表面几何和映射的无穷小层面假设。它具有以下特性。1)它允许以更简单的方式推导出现有解决方案。2)它以统一的方式对SfT和NRSfM进行建模。3)它使我们能够引入一种新的无偏斜变形模型,并为之求解SfT和NRSfM。4)它有助于对SfT进行通用求解,而无需进行变形建模。我们的框架是完整的:它解决了包括等距模型在内的一整类代数变形模型的可变形三维重建问题。我们将我们的解决方案与当前的先进方法进行了比较,结果表明我们的方法在准确性和计算时间方面均更胜一筹。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验