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摘要: 针对从给定2D特征点的单目图像中重构对象的3D形状问题，本文在形状空间模型的基础上，结合L1/2正则化和谱范数的性质提出一种基于L1/2正则化的凸松弛方法，将形状空间模型的非凸求解问题通过凸松弛方法转化为凸规划问题；在采用ADMM算法对凸规划问题进行优化求解过程中，提出谱范数近端梯度算法保证解的正交性与稀疏性.利用所提的优化方法，基于形状空间模型和3D可变形状模型在卡内基梅隆大学运动捕获数据库上进行3D人体姿态重构，定性和定量对比实验结果表明本文方法均优于现有的优化方法，验证了所提方法的有效性.Abstract: In order to estimate the 3D shape with the given 2D feature points for a single image, we present a convex relaxation approach based on L1/2 regularization by using the shape space model and combining L1/2 regularization and the properties of spectral norm. Thereby we transform the non-convex optimization problem in a shape space model into a convex programming problem. When optimizing the solution to the convex programming problem by using the ADMM algorithm, we further propose a spectral norm proximal operator to satisfy the orthogonality and sparsity constraint of the solution. Using the proposed optimization algorithm, we conduct experiments on the CMU motion capture dataset for 3D human body pose reconstruction based on the shape space model and 3D deformable shape model. Comparison experimental results show qualitatively and quantitatively that the proposed algorithm outperforms the existing optimization algorithms. The effectiveness of the proposed algorithm is validated.
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Key words:
- 3D reconstruction /
- sparse representation /
- L1/2 regularization /
- convex program
1) 本文责任编委 杨健 -
图 1 三种方法的定性实验效果对比图
Fig. 1 The comparison of qualitative experiment results of three methods
图 5 三种方法的定性实验效果对比图
Fig. 5 The qualitative experiment effect contrast chart of the three methods
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