Robust 3D Reconstruction with Outliers Using RANSAC Based Singular Value Decomposition(Image Recognition, Computer Vision)

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概要

• 論文の詳細を見る

• It is well known that both shape and motion can be factorized directly from the measurement matrix constructed from feature points trajectories under orthographic camera model. In practical applications, the measurement matrix might be contaminated by noises and contains outliers. A direct SVD (Singular Value Decomposition) to the measurement matrix with outliers would yield erroneous result. This paper presents a novel algorithm for computing SVD with outliers. We decompose the SVD computation as a set of alternate linear regression subproblems. The linear regression subproblems are solved robustly by applying the RANSAC strategy. The proposed robust factorization method with outliers can improve the reconstruction result remarkably. Quantitative and qualitative experiments illustrate the good performance of the proposed method.

• 一般社団法人電子情報通信学会の論文
• 2005-08-01

著者

• Li Xi

  Tsukuba Univ. Tsukuba‐shi Jpn

• Xiang Liuwei

  Xi'an Jiaotong University

• Ning Zhengnan

  Xi'an Jiaotong University

関連論文

• Robust Multi-Body Motion Segmentation Based on Fuzzy k-Subspace Clustering(Image Recognition, Computer Vision)
• Robust 3D Reconstruction with Outliers Using RANSAC Based Singular Value Decomposition(Image Recognition, Computer Vision)

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