Photometric Stereo via Random Sampling and Tensor Robust Principal Component Analysis

机译：通过随机采样和张量鲁棒主成分分析的光度立体

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In this paper, we propose a method for accurate 3D reconstruction based on Photometric Stereo. Instead of applying the global least square solution on the entire over-determined system, we randomly sample the images to form a set of overlapping groups and recover the surface normal for each group using the least square method. We then employ four-dimensional Tensor Robust Principal Component Analysis (TenRPCA) to obtain the accurate 3D reconstruction. Our method outperforms global least square in handling sparse noises such as shadows and specular highlights. Experiments demonstrate the reconstruction accuracy of our approach.

机译：在本文中，我们提出了一种基于光度立体的精确3D重建方法。我们没有在整个超定系统上应用全局最小二乘解，而是对图像进行随机采样以形成一组重叠的组，并使用最小二乘法恢复每个组的表面法线。然后，我们使用三维张量鲁棒主成分分析（TenRPCA）获得准确的3D重建。我们的方法在处理诸如阴影和镜面高光之类的稀疏噪声方面优于全局最小二乘。实验证明了我们方法的重建精度。

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《International conference on graphic and image processing》|2017年|1061539.1-1061539.8|共8页
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Yakun Ju; Lin Qi; Hao Fan; Liang Lu; Junyu Dong;
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Photometric stereo; random sampling; tensor robust principal component analysis; low-rank tensor recovery; four-dimensional tensor;

机译：光度立体;随机抽样;张量鲁棒主成分分析;低秩张量恢复;四维张量;

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专利

1. Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm [J] . IEEE Transactions on Pattern Analysis and Machine Intelligence . 2020,第4期

机译：采用新的Tensor核规范的Tensor鲁棒主成分分析
2. Low-rank tensor train for tensor robust principal component analysis [J] . Yang Jing-Hua, Zhao Xi-Le, Ji Teng-Yu, Applied mathematics and computation . 2020,第1期

机译：用于张力稳健的主成分分析的低级张力列车
3. Accelerating the 3D T 1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model [J] . Yuanyuan Liu, Leslie Ying, Weitian Chen, Quantitative Imaging in Medicine and Surgery . 2021,第8期

机译：使用信号补偿的鲁棒张量主体分析模型加速软骨3D T1ρ映射
4. Photometric Stereo via Random Sampling and Tensor Robust Principal Component Analysis [C] . Yakun Ju, Lin Qi, Hao Fan, International Conference on Graphic and Image Processing . 2017

机译：光度立体声通过随机采样和张力鲁棒主成分分析
5. Instability in principal component analysis: Forming a consensus principal component from multiple sample principal components. [D] . Spurgin, William D. 2016

机译：主成分分析中的不稳定性：由多个样本主成分组成一个共识主成分。
6. Robust principal component analysis for accurate outlier sample detection in RNA-Seq data [O] . Xiaoying Chen, Bo Zhang, Ting Wang, 2020

机译：RNA-SEQ数据中精确异常样本检测的强大主成分分析
7. Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm [O] . Canyi Lu, Jiashi Feng, Yudong Chen, 2020

机译：张量鲁棒主成分分析，具有新的张量核标准

Photometric Stereo via Random Sampling and Tensor Robust Principal Component Analysis

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