We propose a robust method for estimating dynamic 3D curvilinear branching
structure from monocular images. While 3D reconstruction from images has been
widely studied, estimating thin structure has received less attention. This
problem becomes more challenging in the presence of camera error, scene motion,
and a constraint that curves are attached in a branching structure. We propose
a new general-purpose prior, a branching Gaussian processes (BGP), that models
spatial smoothness and temporal dynamics of curves while enforcing attachment
between them. We apply this prior to fit 3D trees directly to image data, using
an efficient scheme for approximate inference based on expectation propagation.
The BGP prior's Gaussian form allows us to approximately marginalize over 3D
trees with a given model structure, enabling principled comparison between tree
models with varying complexity. We test our approach on a novel multi-view
dataset depicting plants with known 3D structures and topologies undergoing
small nonrigid motion. Our method outperforms a state-of-the-art 3D
reconstruction method designed for non-moving thin structure. We evaluate under
several common measures, and we propose a new measure for reconstructions of
branching multi-part 3D scenes under motion.

本文提出了一种基于分支高斯过程（BGP）的方法，在考虑各种约束和误差的情况下，利用图像数据还原带有分支的三维曲线结构，并成功地应用于拍摄多视角植物图像数据的实验中，取得了较好的效果。